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Surface-enhanced Raman spectroscopy (SERS) has been transformed into a useful analytical technique with significant advantages in relation to sensitive and low-concentration chemical analyses. However, SERS substrates are expensive and the analyte sample preparation is complicated; hence, it is only used in limited areas. We have fabricated a tattoo paper-based SERS substrate by using non-complicated inkjet printing. The sensitivity of the SERS substrate was increased by removing the carbon residues via exposure to ultraviolet light without damaging the substrate. Thus, low concentrations of pesticides (up to 1 μM thiram) were measured. The SERS substrate was attached to the curved surface of an apple to demonstrate its advantages, such as the flexibility and easy attachability of tattoo paper, and its feasibility was verified by measuring 1 μM thiram on the apple's surface. Due to its economic cost, simple usage, and rapid measurement, it will be helpful for the identification of both agricultural adulterants and food adulterants and for water-based pollutant detection. It will also possibly be helpful for medical purposes related to human body surfaces in the future. [ABSTRACT FROM AUTHOR]

This article presents perennial grasses, without whose presence it is impossible to imagine the natural environment as well as agriculture, recreation, sport, and satisfactory aesthetics of the environment. Grasses have by far the widest distribution of all flowering families, grow on every continent, and are part of all the major biomes of the terrestrial world. They not only occur in almost all types of natural landscapes but also find a prominent place in the agricultural landscape. Grasses are not only a source of food for people (wheat, rice, maize, millet, etc.) and feed for livestock, but also a source of energy, building materials, a component of paper pulp, etc. Moreover, grasses have numerous uses to enhance the beauty of the surrounding landscape, bring relaxation, health, and comfort to people (i.e., gardens, parks, and sports facilities), and support land protection. This article describes just these, not often mentioned, and characterized grass uses, with an emphasis on the relationship between different species of perennial grasses and their functionality. The aim is to show the various aspects of the amenity use of grasses in the context of species diversity and their future under the conditions of a changing climate. [ABSTRACT FROM AUTHOR]

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry. [ABSTRACT FROM AUTHOR]

As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

Deep learning technology has made significant progress in agricultural image recognition tasks, but the parameter adjustment of deep models usually requires a lot of manual intervention, which is time-consuming and inefficient. To solve this challenge, this paper proposes an adaptive parameter tuning strategy that combines sine–cosine algorithm with Tent chaotic mapping to enhance sea horse optimization, which improves the search ability and convergence stability of standard sea horse optimization algorithm (SHO). Through adaptive optimization, this paper determines the best parameter configuration in ResNet-50 neural network and optimizes the model performance. The improved ESHO algorithm shows superior optimization effects than other algorithms in various performance indicators. The improved model achieves 96.7% accuracy in the corn disease image recognition task, and 96.4% accuracy in the jade fungus image recognition task. These results show that ESHO can not only effectively improve the accuracy of agricultural image recognition, but also reduce the need for manual parameter adjustment. [ABSTRACT FROM AUTHOR]

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

Simple Summary: The digitization of herbarium collections is an important process that allows access to sometimes huge amounts of data that can be used in various natural sciences. In recent years, the collections of one of the thirty oldest herbariums in the world—the herbarium of the Jagiellonian University—have been digitized. This paper presents the resources of the economically very important Amaranthaceae family, which include 8801 herbarium sheets. They were analyzed in taxonomic, geographical, historical, and functional terms. Herbaria constitute a form of documentation, store and secure comparative material, as well as constitute an extra original gene bank. They are an invaluable database among others for the biological, ethnobotanical and agricultural sciences. The digitization of herbarium collections significantly facilitates access to archival materials; however, searching them is still time-consuming. Therefore, our work aims to analyze the herbarium collection of 8801 sheets for specimens representing the economically important family Amaranthaceae (Chenopodiaceae-Amaranthaceae clade) deposited the oldest herbarium in Poland, the herbarium of the Jagiellonian University (KRA). These specimens have been collected from almost all the continents in dozens of countries for over 200 years. The analyses conducted, including the taxonomic coverage, geographical characteristics and origin, temporal coverage and utility importance of representative species, present the discussed resources in a more accessible way and may become a more attractive form for scientists potentially interested in more advanced research work. [ABSTRACT FROM AUTHOR]

The current geopolitical and socioeconomic landscape creates a difficult and uncertain operating environment for farming and agri-food businesses. Technological innovation has not been suggested to be a "silver bullet" but is one of the ways organizations can seek to reduce environmental impact, deliver net zero, address the rural skills and labor deficit and produce more output from fewer resources and as a result, make space for nature. But what barriers limit this promissory narrative from delivering in practice? The purpose of the paper is to firstly explore the reported social, technical and systemic barriers to agri-technology adoption in an increasingly uncertain world and then secondly identify potential research gaps that highlight areas for future research and inform key research questions. Socio-technical and infrastructural barriers have been identified within the context of the complex hollowing out and infilling of rural communities across the world. These barriers include seventeen factors that emerge, firstly those external to the farm (economic conditions, external conditions including bureaucracy, market conditions, weather uncertainty and the narratives about farmers), those internal to the farm business (farming conditions, employee relations, general finance, technology and time pressures) and then personal factors (living conditions, personal finances, physical health, role conflict, social isolation and social pressure). Adaptive resilience strategies at personal, organizational and community levels are essential to address these barriers and to navigate agri-technology adoption in an uncertain and dynamic world. [ABSTRACT FROM AUTHOR]

Potential soil production is closely related to the physical and mechanical properties. The aim of this paper was to evaluate the effect of different levels of soil compaction created by tractor chassis. The total area of the experimental plot was 13.22 ha. Up until 2019, a conventional tillage system had been used. The measurements were carried out with an innovative measuring device that allows for the continuous measurement of the horizontal penetrometry for comparative measurements while driving, which was designed at the Slovak University of Agriculture in Nitra. The measuring device measured the soil resistance in the tire track (On-track) and out of track (Off-track) as well as in three (50 s) sequences within one tractor pass. Three lines were chosen, where in each a pair of combinations was made. The results were subjected, in addition to graphical evaluation, to single factor ANOVA analysis. When comparing individual passes (PH1 to PH6), the statistical analysis showed that the results of the horizontal resistance measurements proved to be statistically significant (p < 0.05) with respect to the weight, number of passes, and tire underinflation. The highest compaction was caused by the first pass, while the higher weight of the tractor during the next pass had a smaller effect. Underinflating the tires ensured a reduction in compaction. Reducing the tractor tire pressure to 0.15 MPa resulted in a reduction in soil compaction of up to 16%. [ABSTRACT FROM AUTHOR]

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

Producers of biofuels for the EU market may use 'low ILUC-risk' certification as evidence that they have not deprived other economic sectors of feedstock material, and hence that indirect land use change (ILUC) emissions have been avoided. At present, the uptake of low ILUC-risk certification is limited to a handful of niche projects, as there is little commercial incentive for obtaining certification. This may be considered a missed opportunity, because low ILUC-risk farming methods offer a range of sustainability co-benefits beyond the mitigation of ILUC emissions. This paper examines the policy foundations of low ILUC-risk and develops policy recommendations that would aim to confer advantages to low ILUC-risk biofuels. Some weaknesses in the low ILUC-risk system's environmental safeguards are also highlighted. [ABSTRACT FROM AUTHOR]

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin–Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs. [ABSTRACT FROM AUTHOR]

The traditional aviary decontamination process involves farmers applying pesticides to the aviary's ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O3) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot's navigation uses a back-and-forth motion that parallels the aviary's longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries' standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment. [ABSTRACT FROM AUTHOR]

This document discusses the importance of microbial communities in agriculture and the use of metagenomic analysis to understand and utilize the agricultural microbiome. Microorganisms in the soil, such as bacteria and fungi, play a crucial role in nutrient cycling, plant growth promotion, disease control, and resilience to environmental stresses. The document highlights various research papers that explore the impact of microorganisms on crop management, disease dynamics, and soil health. The findings emphasize the potential of metagenomic technologies in advancing sustainable farming practices and promoting ecological sustainability. The document also provides links to a special issue on the topic and invites further research contributions. [Extracted from the article]

Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors and lack detailed representations of agricultural shadows with possible human activity inside, excluding those derived from satellite or drone views. In this paper, we present an evaluation of a synthetically generated top-down shadow segmentation dataset characterized by photorealistic rendering and accurate shadow masks. We aim to determine its efficacy compared to real-world datasets and assess how factors such as annotation quality and image domain influence neural network model training. To establish a baseline, we trained numerous baseline architectures and subsequently explored transfer learning using various freely available shadow datasets. We further evaluated the out-of-domain performance compared to the training set of other shadow datasets. Our findings suggest that AgroSegNet demonstrates competitive performance and is effective for transfer learning, particularly in domains similar to agriculture. [ABSTRACT FROM AUTHOR]

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets. [ABSTRACT FROM AUTHOR]

The article discusses the growing interest in soil health and sustainable agriculture as a response to the challenges facing modern agriculture. It highlights innovative strategies such as precision agriculture, organic farming practices, and the integration of biofertilizers. The article categorizes the research papers in the special issue into four themes: nutrient management and soil health, soil management and agricultural sustainability, soil contamination and phytoremediation, and soil nitrogen management and environmental impact. The research papers explore various aspects of these themes, providing insights into enhancing soil productivity and promoting long-term environmental sustainability. The article emphasizes the importance of nurturing soil health and sustainability in the face of increasing demands for food production, environmental conservation, and climate change mitigation. [Extracted from the article]

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences. [ABSTRACT FROM AUTHOR]

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino's effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers' livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. [ABSTRACT FROM AUTHOR]

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land. [ABSTRACT FROM AUTHOR]

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F 1 -scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model's performance, emphasizing the effectiveness of these components in capturing detailed contextual cues. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the evolutionary implications of the correlation between different species of Peucedanum plants and the distribution of Gortyna borelii moth populations in Romania. We highlight geographic separation and isolation among these populations due to anthropogenic landscape fragmentation, which hinders genetic exchange. A geometric morphometric analysis was utilized to visualize and compare the morphometric variations in relation to the environmental variables, particularly the host plant. Additionally, the distribution of G. borelii populations across Europe and in Romania that are correlated with the host plant was analyzed. The significant morphological and morphometric differences between the analyzed populations support our working hypothesis, according to which the use of different Peucedanum species by the larvae of G. borelii leads to an intraspecific diversification correlated with the host plant species. The newly discovered population of G. borelii in Romania holds substantial conservation importance, necessitating protection measures, including demarcating habitat areas and raising awareness among stakeholders. G. borelii is a protected species at the European level (Habitats Directive 92/43/EEC, Appendices II and IV), considered endangered due to the isolation of its populations and anthropogenic pressures exerted through agricultural practices. Understanding the impact of agricultural practices on their habitat is crucial for effective management strategies. Overall, this study sheds light on the complex interplay between ecological adaptation, host plant specialization, and speciation dynamics in phytophagous insects, emphasizing the importance of conservation efforts to preserve G. borelii populations and their habitats. [ABSTRACT FROM AUTHOR]

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming. [ABSTRACT FROM AUTHOR]

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the gross value added (GVA) of Greece's agricultural sector and the GVAs of the other sectors. The research considers both the relationship between value levels and the cycles of GVAs. Dynamic analysis using ARDL modeling shows that there is no cointegration between agricultural GVA and the other GVAs. However, there is an estimated cointegrating relationship between business cycles of agriculture and those of the rest of the economic sectors, with the cycles of services being the significant variable. Moreover, econometric analysis using NARDL modeling shows that there is a cointegrating relationship between the levels of GVAs as well, when asymmetricity—with respect to GVA changes of the services sector—is introduced. [ABSTRACT FROM AUTHOR]

Simple Summary: In major carp farming regions in Bangladesh, around half of the 231 farms assessed reported disease outbreaks, with the factors influencing disease including geographical region, species stocked, and biosecurity practices. Surveys indicated appropriate disinfection measures were highly successful in preventing diseases, but more generally, there was widespread improper use of chemicals and antibiotics. The study underscores the importance of enhanced in-country training and awareness programs to address biosecurity challenges, in turn ensuring the well-being of farmers and communities while promoting sustainable aquaculture. Small-scale carp polyculture plays a key role in food supply in Bangladesh. However, factors including water pollution, limited infrastructure, and inadequate disease management hinder its sustainability. This paper reports on a survey of 231 farmers across the six major carp producing regions in Bangladesh, analyzing factors including farmers' social aspects, farm characteristics, information on disease and approaches adopted to combat them, and biosecurity practices. Almost half (46.8%) of the farms surveyed experienced disease in carp species, with clear regional variations. Eighty-four percent of farms reported carp mortalities during disease outbreaks, with an average mortality level of 10.23 ± 11.81%. Clinical signs during outbreaks lasted between a week and a month, with a peak in disease outbreaks occurring in two seasonal periods between June and July and October and December. Disease incidence was related to a range of factors including the farmer's experience, ponds/farm type, stocked species, and biosecurity practice. A combination of disinfecting measures during pond preparation and measures during stocking, including discarding fingerling transport water away from the farm, fingerling disinfection, and checking the health of fingerlings before stocking, significantly reduced disease occurrence. Treatments involving antibiotics, ciprofloxacin, erythromycin, and azithromycin were reported as ineffective, raising concerns about their non-prudent use, inadequate dosing (perhaps without appropriate veterinary guidance), and the potential for driving antimicrobial resistance in the environment. The research unveils a concerning pattern of high disease incidence across small-scale carp farms in Bangladesh, and the significant potential for disease spread highlights the need for responsible disposal practices. The study emphasizes the need for improving training and awareness programs for addressing biosecurity and disease management challenges, ensuring sustainable aquaculture and community well-being. [ABSTRACT FROM AUTHOR]

Simple Summary: This work aims to analyze flocks' management practices and sheep breeders' perceptions of the impact of climate change to enhance the resilience of the sheep production systems in the Tunisian semi-arid region. The results revealed three main sheep production systems: the agro-pastoral rain-fed system (AGPRF), the agro-pastoral irrigated system (AGPI), and the agro-sylvo-pastoral system (AGSP). Each production system is characterized by specific management and productive parameters that are used by farmers to build resilience actions. However, farmers' climate change perceptions are mostly the same across the three sheep production systems: a decrease in precipitation and an increase in temperatures and extreme events, which negatively influence feedstuff availability and costs. Global climate change inflicts unambiguous risks on agricultural systems and food security. Small ruminants are known for their ability to adapt to changing environmental conditions. This paper aims to characterize sheep production systems in a vulnerable agricultural zone and the breeders' perceptions of climate change to apprehend challenges that they are confronting, and formulate resilience actions. The data analysis is based on 94 semi-structured surveys of sheep farmers carried out in the Tunisian semi-arid region. The PCA analysis results revealed three main sheep production systems. The agro-pastoral rain-fed system (AGPRF) is dominant (55%), with large farms and common pastures integrating cereals and fodder. The agro-pastoral irrigated system (AGPI: 20%) is characterized by small-area and forage irrigation (1.8 ha) and a smaller number of ewes but a greater use of animal feed supplementation. The agro-sylvo-pastoral system (AGSP: 25%) is a system where grazing is based on common lands and using tree sub-products, while the agricultural area is exclusively used to cultivate cereal crops. Sheep breeders' climate perceptions are summarized as unpredictable climate events, a decrease in precipitation, and an increase in temperature. Resilience actions principally consist of reducing flocks' numbers, using alternative local feed, fodder, and water resources, and building more shelters and planting more trees in the grazing areas. Nevertheless, cost-effectiveness should be considered in such vulnerable zones to insure the sheep production systems' sustainability. [ABSTRACT FROM AUTHOR]

Corn and soybeans play pivotal roles in the agricultural landscape of the United States, and accurately delineating their cultivation areas is indispensable for ensuring food security and addressing hunger-related challenges. Traditional methods for crop mapping are both labor-intensive and time-consuming. Fortunately, the advent of high-resolution imagery, exemplified by Sentinel-2A (S2A), has opened avenues for precise identification of these crops at a field scale, with the added advantage of cloud computing. This paper presents an innovative algorithm designed for large-scale mapping of corn and soybean planting areas on the Google Cloud Engine, drawing inspiration from symmetrical theory. The proposed methodology encompasses several sequential steps. First, S2A data undergo processing incorporating phenological information and spectral characteristics. Subsequently, texture features derived from the grayscale matrix are synergistically integrated with spectral features in the first step. To enhance algorithmic efficiency, the third step involves a feature importance analysis, facilitating the retention of influential bands while eliminating redundant features. The ensuing phase employs three base classifiers for feature training, and the final result maps are generated through a collective voting mechanism based on the classification results from the three classifiers. Validation of the proposed algorithm was conducted in two distinct research areas: Ford in Illinois and White in Indiana, showcasing its commendable classification capabilities for these crops. The experiments underscore the potential of this method for large-scale mapping of crop areas through the integration of cloud computing and high-resolution imagery. [ABSTRACT FROM AUTHOR]

The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute force-based K-fold cross-validation or the naivety of hold-out are required, with the following main goals in mind: (1) carrying out one-shot, close-to-optimal data arrangements, accelerating conventional training optimization; and (2) aiming at maximizing the capacity of inference models to its fullest extent while relieving computational burden. To that end, in this paper, two image-based feature-aware dataset splitting approaches are proposed, hypothesizing a contribution towards attaining classification models that are closer to their full inference potential. Both rely on strategic image harvesting: while one of them hinges on weighted random selection out of a feature-based clusters set, the other involves a balanced picking process from a sorted list that stores data features' distances to the centroid of a whole feature space. Comparative tests on datasets related to grapevine leaves phenotyping and bridge defects showcase promising results, highlighting a viable alternative to K-fold cross-validation and hold-out methods. [ABSTRACT FROM AUTHOR]

Photosynthetically Active Radiation (PAR) is an important parameter in the plant photosynthesis process, which can relate to plant growth, crop water use, and leaf gas exchange. Previously, many researchers utilized commercially available sensors to monitor PAR. The high cost of the commercially available PAR sensors has limited researchers, agricultural professionals, and farmers to use and expand PAR monitoring in agricultural lands. Thus, this paper focuses on designing an affordable Internet of Things (IoT)-based PAR sensor monitoring system including 3D-printed enclosures (waterproof) for the sensors, performance evaluation of multiple light sensors, solar powering configuration, cloud setup, and cost analysis. Three sensors, including VTB8440BH photodiode, SI 1145, and LI-190R sensors, were evaluated. The 3D-printed waterproof enclosures were designed for the photodiode and SI 1145. Particle Boron was used for recording and sending the sensor data to the IoT webserver. Both the photodiode and SI 1145 were compared to LI-190R, which is the industry standard. In the calibration process, the R2 values of the photodiode and SI 1145 with LI-190R were 0.609 and 0.961, respectively. Field validation data shows that SI 1145 had a strong correlation with LI-190R. In addition, the performance evaluation data shows the photodiode had a weaker correlation with LI-190R than SI 1145. In conclusion, the study successfully developed and designed affordable and reliable IoT-based PAR sensor monitoring systems, including a 3D-printed housing, hardware, programming, and IoT website. SI 1145 with a glass filter is an alternative sensor to monitor PAR at a low cost and has the advantage of being connected to IoT microcontrollers. [ABSTRACT FROM AUTHOR]

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties. [ABSTRACT FROM AUTHOR]

To control heavy metal pollution effectively, prevention of heavy metal accumulations in agricultural soils should be the priority rather than remediation of heavy metal contamination. In this research, papers which contained input and output fluxes of Cd, As, Cr, Hg, and Pb in topsoil (the plough layer) of agricultural lands in Hunan, Zhejiang, the Yangtze River Delta, Hainan, and China as a whole were explored. Fluxes of heavy metal species were recalculated, and future trends of pollution were predicted. Also, cases regarding the application of technologies to control the input and output of heavy metals were analyzed. Results indicated that atmospheric decomposition was the dominant input source of heavy metals in all study sites except Hainan. The relative contributions of fertilizers, irrigation water, and straw returning fluctuated greatly among different sites. Cd pollution in all sites was the most serious, followed by Cr and Pb. In Hunan, Cd has already exceeded the maximum limit value and needs to be controlled urgently. The input of heavy metals from irrigation water, fertilizers, and straw returning could be controlled by proposing more policies to manage their quality and application amounts. The amount of heavy metals absorbed by plants could be increased by cultivating crops with hyperaccumulators and high-biomass plants. [ABSTRACT FROM AUTHOR]

The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method. [ABSTRACT FROM AUTHOR]

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture. [ABSTRACT FROM AUTHOR]

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management. [ABSTRACT FROM AUTHOR]

Simple Summary: Lignocellulose, consisting of cellulose, hemicellulose, and lignin, constitutes 60% of Earth's biomass and plays a critical role in the carbon cycle. Abundantly found in plant leaves and stems, cellulose undergoes biodegradation predominantly by cellulolytic microorganisms that produce cellulases. This process is particularly vital for the breakdown of crystalline cellulose in plant cell walls. The effective degradation of cellulose in natural environments hinges on the accurate identification of truly cellulolytic bacteria. This review compiles and analyzes data from the past 11 years on such bacteria, derived from forest and agricultural soils, and offers insights into the functions of cellulolytic bacteria and their cellulase enzymes. This review provides insights into cellulolytic bacteria present in global forest and agricultural soils over a period of 11 years. It delves into the study of soil-dwelling cellulolytic bacteria and the enzymes they produce, cellulases, which are crucial in both soil formation and the carbon cycle. Forests and agricultural activities are significant contributors to the production of lignocellulosic biomass. Forest ecosystems, which are key carbon sinks, contain 20–30% cellulose in their leaf litter. Concurrently, the agricultural sector generates approximately 998 million tons of lignocellulosic waste annually. Predominant genera include Bacillus, Pseudomonas, Stenotrophomonas, and Streptomyces in forests and Bacillus, Streptomyces, Pseudomonas, and Arthrobacter in agricultural soils. Selection of cellulolytic bacteria is based on their hydrolysis ability, using artificial cellulose media and dyes like Congo red or iodine for detection. Some studies also measure cellulolytic activity in vitro. Notably, bacterial cellulose hydrolysis capability may not align with their cellulolytic enzyme production. Enzymes such as GH1, GH3, GH5, GH6, GH8, GH9, GH10, GH12, GH26, GH44, GH45, GH48, GH51, GH74, GH124, and GH148 are crucial, particularly GH48 for crystalline cellulose degradation. Conversely, bacteria with GH5 and GH9 often fail to degrade crystalline cellulose. Accurate identification of cellulolytic bacteria necessitates comprehensive genomic analysis, supplemented by additional proteomic and transcriptomic techniques. Cellulases, known for degrading cellulose, are also significant in healthcare, food, textiles, bio-washing, bleaching, paper production, ink removal, and biotechnology, emphasizing the importance of discovering novel cellulolytic strains in soil. [ABSTRACT FROM AUTHOR]

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset. [ABSTRACT FROM AUTHOR]

Industry 4.0 marks a major technological shift, revolutionizing manufacturing with increased efficiency, productivity, and sustainability. This transformation is paralleled in agriculture through smart farming, employing similar advanced technologies to enhance agricultural practices. Both fields demonstrate a symmetry in their technological approaches. Recent advancements in software engineering and the digital twin paradigm are addressing the challenge of creating embedded software systems for these technologies. Digital twins allow full development of software systems before physical prototypes are made, exemplifying a cost-effective method for Industry 4.0 software development. Our digital twin prototype approach mirrors software operations within a virtual environment, integrating all sensor interfaces to ensure accuracy between emulated and real hardware. In essence, the digital twin prototype acts as a prototype of its physical counterpart, effectively substituting it for automated testing of physical twin software. This paper discusses a case study applying this approach to smart farming, specifically enhancing silage production. We also provide a lab study for independent replication of this approach. The source code for a digital twin prototype of a PiCar-X by SunFounder is available open-source on GitHub, illustrating how digital twins can bridge the gap between virtual simulations and physical operations, highlighting the symmetry between physical and digital twins. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]

The claim that environmental virtue ethics (EVE) is anthropocentric appears inherently aporetic since it implies that either anthropocentrism is virtuous or the whole environmental issue is anthropocentric, thus translating vices into virtues or vice versa. Another interpretation is that both the environment and humanity are thought with a vicious conception of centeredness. Conversely, if centeredness is rightly addressed and humanity and its environment are considered as one and the same issue, the focus on anthropocentrism should also be different. By drawing on Felix Guatttari's ecosophy, this paper proposes that EVE needs to be based on a philosophical understanding of agriculture. Thus, agriculture is the organic and epistemic matrix of our relation to the environment and not merely a section of an abstract environment nor one economic area among others. The environmental crisis is primarily a crisis of humanity within its agricultural matrix. To be an environmentally virtuous human being, a requirement is to face again the burden of our absolute need for food and for fruitful cooperation between farmers and plants, not only animals. This paper discusses the importance of plant ethics and plant topology to understand the specificities of the agricultural matrix. The emphasis will be placed on plant-centered virtue ethics and reframing anthropocentrism by drawing on transdisciplinary conversation with plant practitioners in the context of a research action project. [ABSTRACT FROM AUTHOR]

Aeroponics is a soilless agricultural technique that grows plants by misting their roots with a nutrient-rich solution. Technology has transformed aeroponics by providing it with benefits such as the control of environmental factors, automated nutrient delivery, and the monitoring of plant health. This paper presents a systematic review of 47 studies to identify the status and tendencies in the usage of technology in aeroponics as well as the main opportunities and challenges. Furthermore, this paper introduces the Technology Adoption and Integration in Sustainable Agriculture (TAISA) model. TAISA is a model that identifies the degree of technology integration in any sustainable agriculture system to determine how technology affects production and quality. The systematic review indicates that the most common technology in aeroponics is sensing technology and Industry 4.0. These technologies have brought multiple benefits such as sustainability and time efficiency. Conversely, the studies highlighted technical complexity and power dependency as the main challenges in technology-assisted aeroponics. Finally, the TAISA model reveals that technology has primarily been employed in creating new processes that are only possible to implement with the help of technology. Therefore, we conclude that technology use has taken root in aeroponics and can be promoted to improve sustainable agriculture. [ABSTRACT FROM AUTHOR]

The worldwide growing demand for food is pushing the agricultural field towards new innovative solutions to increase the efficiency and productivity of cultivations. In this direction, agricultural mechanization plays a crucial role, and tractors are among the most important actors. Agricultural tractors are machines designed to push/pull special instruments usually referred to as implements, to which they may transfer power by means of a mechanical power take-off (PTO) or via hydraulic connections, thanks to the availability of pressurized oil. The tractor can be seen as a mobile power station: the more efficiently it provides power to external implements or to the ground in terms of tractive effort, the higher will be the efficiency and productivity of a certain task. However, the growing demand for greener and sustainable work machines is pushing towards new concepts of tractor powertrains with the goal of reducing, as much as possible, the amount of pollutants and GHG emissions per unit of work. In this paper, the authors will propose a review of the current trends towards electrification of agricultural tractors. Electrification can help in making vehicles more efficient and opening a new scenario for work optimization. Moreover, electrification is also involving the implements attached to the tractor and responsible for actually performing a wide variety of field tasks. However, tractor electrification requires proper attention due to the impact of high power electric systems on the vehicle configuration. For this reason, a proper level of hybridization should be considered. In this paper, a new classification method will be proposed, considering the electrification level in terms of power and as a function of the installed electric energy storage. This definition will be applied to classify the current state of the art of electric and hybrid agricultural tractors, investigating current trends in the scientific community and among industrial manufacturers with a look to the new upcoming technologies. [ABSTRACT FROM AUTHOR]

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services. [ABSTRACT FROM AUTHOR]

Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes. [ABSTRACT FROM AUTHOR]

Measurement of zoo animal welfare states enables improvement to husbandry and management to be evidence-based and implemented according to species' needs. Theoretical welfare concepts are often discussed, and whilst it is helpful to ensure wide consensus across all stakeholders on what welfare comprises, practical application of such evidence-based information is as equally important. All species housed in zoos will have specific needs that must be met by their housing, husbandry, and care to enable them to thrive. Therefore, this paper examined how to identify key animal care needs and an animal's responses to them to form a basis for species-specific welfare assessment approaches. There are examples of familiar-to-the-zoo species that still pose challenges regarding delivery of optimal husbandry and management. As such, the identification and evaluation of core concepts of the biology, behaviour, "needs and wants" of these species is required to support validation and refinement of physical, behavioural, and psychological welfare measures. This article evaluated the use of evidence to build capacity in welfare measurement for a familiar zoo-housed species, the giraffe (Giraffa camelopardalis, Linnaeus 1758), by outlining seven key needs and requirements that must be provided in the zoo ("giraffe W-E-L-F-A-R-E" = Warmth, Enrichment, Leaves, Feeding, Alfalfa, Rumination, Exercise). Provision of these inputs, and opportunities for all giraffes in a herd to engage with them, provides the foundation for further welfare assessment to be implemented. Specifically, the validation and measurement of mental states that are more likely to be positive if key behavioural and ecological needs have already been met. This paper advocated for this evidence-based approach to "welfare-focussed husbandry", with distillation of key information that supports species-relevant care, to be developed for other zoo-housed species as support for their welfare assessment protocols. Such welfare-focussed husbandry is layered on top of the basic animal care requirements of the species in the zoo to ensure all individuals have the best opportunity to attain positive welfare states. In this way, and once validated, foundational welfare assessment can be easily completed by busy animal care staff, capacity is built into zoo operations as all stakeholders are aware of exactly what each species needs, and deeper dive welfare assessment (especially concerning animal mental states) can be targeted more effectively. Further evolution of these seven steps for the giraffe is suggested and extrapolation of this approach, to aid identification of key welfare indicators across all zoo-housed species, is encouraged. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology. [ABSTRACT FROM AUTHOR]