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Research on the quality of the air in rural areas is essential for determining base emissions of air pollutants, evaluating the effects of dust pollutants particular to rural areas, modeling the dispersion of pollutants, and developing appropriate pollution mitigation systems. The absence of a systematic review based on the assessment of air quality levels in agricultural settings based on integrated weather variables and air pollutants in the literature draws attention to the deficiencies and the necessity of further research in this area. Hence, our study aimed to develop an Arduino monitoring system with related sensors to acquire some air pollutants and weather parameters. Additionally, we proposed an innovative solution to compare air quality levels by suggesting a new criterion called an integrated indicator for air quality assessment (IAQA). It was created based on the weighted average method to combine the investigated air pollutants and weather parameters. This criterion was evaluated while conducting field measurements in 29 environmentally different agricultural regions located within the Kingdom of Saudi Arabia. To determine the integrated indicator, all the values of the variables were normalized between 0 and 1. The agricultural setting with the lowest integrated indicator was the best environmentally. The lowest and highest values of the integrated indicator ranged from 37.03% and 66.32%, respectively, with an arithmetic average of 48.24%. The developed criterion can change its value depending on the change in the weight value of the variables involved, and it is suitable for application to any other agricultural or non-agricultural setting to evaluate the pollution level in the air. Although similar research has been published, this paper presents novelty findings based on integrated values of air pollutants and weather variables for defining a new criterion called IAQA. Additionally, this paper presents original results for air pollutants and weather aspects in different agricultural settings. [ABSTRACT FROM AUTHOR]

Blockchain is a distributed, immutable ledger technology initially developed to secure cryptocurrency transactions. Following its revolutionary use in cryptocurrencies, blockchain solutions are now being proposed to address various problems in different domains, and it is currently one of the most "disruptive" technologies. This paper presents a scoping review of the scientific literature for exploring the current research area of blockchain applications in the agricultural sector. The aim is to identify the service areas of agriculture where blockchain is used, the blockchain technology used, the data stored in it, its combination with external databases, the reason it is used, and the variety of agricultural products, as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR methodology. The purpose of conducting these scoping reviews is to identify the evidence in this field and clarify the key concepts. The literature search was conducted in April 2021 using Scopus and Google Scholar, and a systematic selection process identified 104 research articles for detailed study. Our findings show that in the field, although still in the early stages, with the majority of the studies in the design phase, several experiments have been conducted, so a significant percentage of the work is in the implementation or piloting phase. Finally, our research shows that the use of blockchain in this domain mainly concerns the integrity of agricultural production records, the monitoring of production steps, and the monitoring of products. However, other varied and remarkable blockchain applications include incentive mechanisms, a circular economy, data privacy, product certification, and reputation systems. This study is the first scoping review in this area, following a formal systematic literature review methodology and answering research questions that have not yet been addressed. [ABSTRACT FROM AUTHOR]

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection. [ABSTRACT FROM AUTHOR]

The use of agricultural films, pesticides, and fertilizers has increased in China, leading to the degradation of the agricultural ecological environment. Based on the panel data of 30 provinces in China from 2010 to 2021, this paper takes agricultural film usage as the threshold variable to study the impact of pesticide and fertilizer usage on agricultural development. The research results show that a single threshold effect of agricultural film usage is significant. When agricultural film usage is both a core and threshold variable, that is lower than the threshold value, every 1% increase in agricultural film usage will reduce agricultural output value by 0.9862%, and the impact is significant. When agricultural film usage crosses the threshold value, every 1% increase in agricultural film usage will increase agricultural output value by 1.1843%, and the impact is significant. When pesticide usage is the core variable, regardless of whether agricultural film usage is below or above the threshold, pesticide usage promotes agricultural development. Its impact is significant. When fertilizer input is a core variable, regardless of whether agricultural film usage is below or above the threshold, the use of fertilizers hinders agricultural development. The impact of fertilizer input on agricultural development is significant. Through empirical testing, this paper explores the deep relationship between agricultural film usage, pesticide usage and fertilizer input on agricultural development. These can effectively allocate agricultural production factors, transform the mode of agricultural economic growth, achieve the coordinated development of the ecological environment and agricultural economy and provide data support for the formulation of agricultural green development policies. [ABSTRACT FROM AUTHOR]

The area of remote sensing techniques in agriculture has reached a significant degree of development and maturity, with numerous journals, conferences, and organizations specialized in it. Moreover, many review papers are available in the literature. The present work describes a literature review that adopts the form of a systematic mapping study, following a formal methodology. Eight mapping questions were defined, analyzing the main types of research, techniques, platforms, topics, and spectral information. A predefined search string was applied in the Scopus database, obtaining 1590 candidate papers. Afterwards, the most relevant 106 papers were selected, considering those with more than six citations per year. These are analyzed in more detail, answering the mapping questions for each paper. In this way, the current trends and new opportunities are discovered. As a result, increasing interest in the area has been observed since 2000; the most frequently addressed problems are those related to parameter estimation, growth vigor, and water usage, using classification techniques, that are mostly applied on RGB and hyperspectral images, captured from drones and satellites. A general recommendation that emerges from this study is to build on existing resources, such as agricultural image datasets, public satellite imagery, and deep learning toolkits. [ABSTRACT FROM AUTHOR]

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control. [ABSTRACT FROM AUTHOR]

Achieving automatic question-and-answering for agricultural scenarios based on machine reading comprehension can facilitate production staff to query information and process data efficiently. Nevertheless, when studying agricultural question-and-answer classification, there are barriers, such as small-scale corpus, narrow content range of corpus, or the need for manual annotation. In the context of such production needs, this paper proposed a text classification model based on text-relational chains and applied it to machine reading comprehension and open-ended question-and-answer tasks in agricultural scenarios. This paper modified the BERT network based on semi-supervised and contrastive learning to enhance the model's performance. By incorporating the text-relational chains with the BERT network, the Chains-BERT model is constructed. Our efficient mode method outperformed other methods on the CAIL2018 dataset. Ultimately, we developed an automatic question-and-answering application to embed the contrastive-learning information aggregation model in this paper. The accuracy of the proposed model exceeded that of several contrasting mainstream models in many open-source datasets. In agricultural scenarios, the model has achieved state-of-the-art levels and is the best in efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: This work could be applied in agricultural sciences related to soil compaction (to determine the parameters causing the soil compaction). Moreover, this work can be interesting from a practical point of view—especially in agricultural practices, to reduce the risk of soil compaction. This paper presents an innovative method to determine the impact of agricultural wheels on soil. The experiment was conducted under controlled conditions, and the parameters of the tire footprints on the soil were analyzed. The tested parameters were the width, length, and depth of the footprint, the cross-section area of the tire, and the area of the footprint. All parameters were determined using the 3D scanning method. Two types of tires, two levels of vertical load, and three levels of inflation pressure were used. The aim of the research was to demonstrate differences in changes in the footprint parameters as a result of changes in the operational parameters of the tires. It was found the bias-ply tire was less responsive to changes in the width and length of the footprint than the radial tire. Moreover, it was shown that radial and bias-ply tires achieved similar values for the footprint area but in the case of bias-ply tires, there was a much greater footprint depth. This means that the side parts of the footprint of bias-ply tires have a more vertical profile, so they carry the vertical loads to a lesser extent. [ABSTRACT FROM AUTHOR]

Featured Application: The research presented by the authors in this article points to the need for changes in the construction of agricultural biogas plants to optimise electricity production. Energy from biogas is widely available, inexpensive, and often contributes to waste management, making it one of the most promising renewable energy sources. The main factors influencing this process' efficiency include the substrates' chemical composition, temperature, and digester load. This paper presents the possibilities offered by a biogas plant built at a farm specialising in dairy cows. The dependence of the power generated in the micro biogas plant on its technical parameters was analysed in detail. Studies carried out by the authors in an agricultural microgas plant (with an electrical output of 40 kW) have shown that they are designed to maintain continuous energy production, despite changing process parameters such as digester mass level, biogas height, temperature or slurry flow into the digester. However, from the point of view of the amount of electricity generated, changes would have to be made to the design of the biogas plant. Firstly, a more powerful generator would have to be installed to cover the electricity requirements of the equipment installed in the biogas plant so that power close to the rated capacity of the biogas plant is still sent to the grid. Secondly, replacing the two existing agitators of the digestion mass (9 kW each) with more agitators of lower power (e.g., four agitators of 4.5 kW each) would be necessary. These should be programmed so that one of the agitators operates at any given time (the operating time of a given agitator should depend on the composition of the digestate). [ABSTRACT FROM AUTHOR]

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model. [ABSTRACT FROM AUTHOR]

Featured Application: The results from this work demonstrate the effective use of machine vision and learning technologies to support the development and implementation of smart agriculture. This paper presents the application of machine vision and learning techniques to detect and identify the number of flower clusters on apple trees leading to the ability to predict the potential yield of apples. A new field robot was designed and built to collect and build a dataset of 1500 images of apples trees. The trained model produced a cluster precision of 0.88 or 88% and a percentage error of 14% over 106 trees running the mobile vehicle on both sides of the trees. The detection model was predicting less than the actual amount but the fruit flower count is still significant in that it can give the researcher information on the estimated growth and production of each tree with respect to the actions applied to each fruit tree. A bias could be included to compensate for the average undercount. The resulting F1-Score of the object detection model was 80%, which is similar to other research methods ranging from an F1-Score of 77.3% to 84.1%. This paper helps lay the foundation for future application of machine vision and learning techniques within apple orchards or other fruit tree settings. [ABSTRACT FROM AUTHOR]

Featured Application: The integrated analysis method for understanding the main driving factors shaping forested areas proposed in this paper can provide helpful insight into sustainable and long-term land use systems in Senegal. Understanding the factors influencing the vulnerability of forested areas is crucial for human well-being and effective governance of ecosystem supply and demand. Based on remote sensing data, this study also considered ten natural and human variables as indexes to explore the main influencing factors that may impact the vulnerability of the Thies region's forested areas. The 2005, 2010, 2015, and 2020 satellite image data were processed using ArcGIS 10.6 and ENVI 5.1 software. The methodology includes using the transfer matrix approach and calculating the geographic landscape index to describe the dominant morphology of forested areas. Furthermore, a mixed linear regression model was built to establish the connection between forested areas and the potential contributing components. Our study revealed that the forested areas led to relative fragmentation, with an average of 88 patches for Aggregation Index (AI), 3.25 for Largest Patch Index (LPI), 2.50 for Patch Density (PD), and 112 for Landscape Shape Index (LSI) between 2005 and 2020. In addition, the transfer matrix indicated that the loss of forestry areas was about −78.8 km2 for agricultural land, −127.8 km2 for bare land, and −65.3 km2 for artificial surfaces. The most critical factors that influenced forested areas were agricultural and manufactural added value, rainfall (p < 0.05), slope, distance to the road, and agricultural sown area (p < 0.001). Overall, this investigation has revealed that the effective management of forested areas in the Thies region requires an understandable assessment. It was observed that both human anthropogenic and natural factors significantly contribute to the decline in forested areas. [ABSTRACT FROM AUTHOR]

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects. [ABSTRACT FROM AUTHOR]

In this paper, a viability study is conducted for a bioremediation intervention in a cultivated area contaminated by a pipeline oil spill. In this context, green technologies such as bioremediation and phytoremediation could represent an optimal solution for reducing pollution without deteriorating soil quality. The phytoremediation test was conducted at the microcosm scale using three plant species (Zea mays, Lupinus albus and Medicago sativa) and at the mesocosm scale (Zea mays), also evaluating the application of plant growth-promoting bacteria (PGPB). The results showed that the selected plants, being able to grow satisfactorily, are able to lessen the presence of hydrocarbons in the soil. An increase of 15–18% in the degradation of the C > 12 fractions in vegetated soils was observed, confirming the effect of plants on the biodegradation of hydrocarbons in the soil. Moreover, a further improvement was recorded after adding PGPB, resulting in fresh biomass production being up to 50% higher than the controls and the degradation of the C > 12 fraction increasing by up to an additional 10%. Particular attention was also paid to pyrene, considered an indicator of PAH contamination. At the end of the experimentation in vegetated soils, pyrene removal reached values above 50%. By favoring plant growth, the addition of PGPB resulted in a further up to 20% reduction in the content of the contaminant in the soil. The primary role of the plants in soil contaminated by petroleum derivatives was to accelerate the degradation of contaminants through the stimulation of microbial activity. Therefore, the cooperation between plants and microorganisms can be concretely used as a nature-based solution in a sustainable and economical way. [ABSTRACT FROM AUTHOR]

The "low indirect land use change risk" ("low ILUC-risk") concept was developed to assess whether crop-based biofuels would compete with other land uses and cause the expansion of agricultural land. At the core of low ILUC-risk is an "additionality principle", which requires that biofuel feedstock receive special treatment only if it is produced over and above the business-as-usual baseline. This paper examines and tests the European Commission's methodology for calculating the baseline for yield improvement projects, by applying it to publicly available Eurostat data at national and NUTS2 scales. We assess from a statistical perspective how variation in regional yield trends would lead to differences in the long-term outcomes of low ILUC-risk certification; we conclude that, as currently designed, the methodology would over-state the amount of additional production in some cases and could hence incentivise the diversion of crops from other uses into the biofuel sector. We introduce the terms "tailwind additionality", "headwind additionality", and "additionality ratchet" to characterise the phenomena which contribute to this outcome. Our results lead us to recommendations which may enhance both the attractiveness and the robustness of the low ILUC-risk system. [ABSTRACT FROM AUTHOR]

Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command. [ABSTRACT FROM AUTHOR]

Lyocell man-made cellulosic fibres (L-MMCF) have been commercially available since the mid-1990's, with the typical feedstock prepared from tree pulp or cotton linters. In recent years, there have been advancements in the utilisation of high alpha-cellulose agricultural biomass for L-MMCF feedstock. Industrial hemp stalks offer a unique opportunity for L-MMCF since hemp is considered an environmentally conscious crop that can also help to bridge the gap in worldwide cellulose shortages; additionally, industrial hemp stalks are high in alpha-cellulose, making this an ideal feedstock for L-MMCF manufacturing. This review paper outlines the lyocell process in detail, including processes for preparation of feedstocks, pulp processing, removal of contaminants and nonessential plant components, pulp dissolution, dope preparation, and fibre spinning. Opportunities and challenges associated with the utilisation of industrial hemp stalks as an alternative feedstock are addressed through all steps of the manufacturing process. Incorporating alternative feedstock opens new perspectives for manufacturing sustainable L-MMCF. [ABSTRACT FROM AUTHOR]

One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse's internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R2, RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year's four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R2 = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of ±2 °C hysteresis. [ABSTRACT FROM AUTHOR]

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively. [ABSTRACT FROM AUTHOR]

Monitoring and control systems integrated into agricultural machinery enable the development of agricultural analyses with advanced management tools, but the full use of all available data is often limited by the lack of uniformity among data transmitted from different agricultural machines. This paper presents an agricultural data aggregation and conversion model that allows for the collection and use of data captured from different agricultural machines in the course of work; these data differ in their original file formats and cannot be combined and used in a common analysis system. Programming work was carried out to create the model, and a specialised software interface enabled raster data processing using a Python library together with the open-source Hypertext Preprocessor and JavaScript programming language libraries. A PostGIS extension was utilised to engage field geometry and map-layering tools. Model validation showed that the data aggregation and conversion functions ensure the evaluation of semantic content and the transformation of the aggregated data into a unified format which is suitable for further use in intelligent farming management applications. The developed model will encourage precision agriculture, with the aim of improving work efficiency and the rational use of resources, the economy, and ecology in agriculture. [ABSTRACT FROM AUTHOR]

As an important economic fish resource, germplasm resources and phenotypic measurements of tilapia are of great importance in the direction of culture and genetic improvement. Furthermore, accurate identification and precise localization of tilapia body parts are crucial for enabling key technologies such as automated capture and precise cutting. However, there are some problems in the semantic segmentation of tilapia fish, including the accuracy of target edge segmentation and the ambiguity in segmenting small targets. To improve the accuracy of semantic segmentation of tilapia parts in real farming environments, an improved Deeplabv3+ network model method is proposed for implementing tilapia part segmentation to facilitate phenotypic measurements on tilapia in this paper. The CBAM module is embedded in the encoder, which can improve the accurate identification and localization of tilapia parts by adaptively adjusting the channel weights and spatial weights and better focus on the key features and spatial connections of tilapia body parts. Furthermore, the decoding part of the Deeplabv3+ model is optimized by using SENet, which greatly increases the segmentation accuracy of the network by establishing the interdependence between channels while suppressing useless features. Finally, model performance is tested and compared with the original network and other methods on the tilapia part segmentation dataset. The experimental results show that the segmentation performance of the improved network is better than other networks, such as PSPNet and U-Net, and the IoU values in the head, fins, trunk, and tail of the fish body are 9.78, 2.27, 6.27, and 6.58 percentage points higher than those of the Deeplabv3+ network, respectively. The results validate the effectiveness of our approach in solving the above problems encountered in the semantic segmentation of tilapia parts. [ABSTRACT FROM AUTHOR]

During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry. [ABSTRACT FROM AUTHOR]

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse. [ABSTRACT FROM AUTHOR]

Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people's dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early. [ABSTRACT FROM AUTHOR]

Agriculture is regarded as one of the key food sources for humans throughout history. In some countries, more than 90% of the population lives on agriculture. However, pests are regarded as one of the major causes of crop loss worldwide. Accurate and automated technology to classify pests can help pest detection with great significance for early preventive measures. This paper proposes the solution of a residual convolutional neural network for pest identification based on transfer learning. The IP102 agricultural pest image dataset was adopted as the experimental dataset to achieve data augmentation through random cropping, color transformation, CutMix and other operations. The processing technology can bring strong robustness to the affecting factors such as shooting angles, light and color changes. The experiment in this study compared the ResNeXt-50 (32 × 4d) model in terms of classification accuracy with different combinations of learning rate, transfer learning and data augmentation. In addition, the experiment compared the effects of data enhancement on the classification performance of different samples. The results show that the model classification effect based on transfer learning is generally superior to that based on new learning. Compared with new learning, transfer learning can greatly improve the model recognition ability and significantly reduce the training time to achieve the same classification accuracy. It is also very important to choose the appropriate data augmentation technology to improve classification accuracy. The accuracy rate of classification can reach 86.95% based on the combination of transfer learning + fine-tuning and CutMix. Compared to the original model, the accuracy of classification of some smaller samples was significantly improved. Compared with the relevant studies based on the same dataset, the method in this paper can achieve higher classification accuracy for more effective application in the field of pest classification. [ABSTRACT FROM AUTHOR]

Featured Application: The paper elaborates on the applicability of blockchain technology in traceability systems of agri-food products. Food holds a major role in human beings' lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain. [ABSTRACT FROM AUTHOR]

The identification of branches and bud points is the key to intelligent pruning of dormant grapevine branches and precise positioning of the pruning point on the branch is an important prerequisite for robotic arm pruning. This study takes Cabernet Sauvignon wine grapes as the experimental object and proposes a depth image-based pruning point localization algorithm based on pruning rules. In order to solve the problem of bud recognition in complex backgrounds, this study adopts a detection method that combines semantic segmentation and target detection. Firstly, the semantic segmentation algorithm PSP-net is used to separate the branches and the main stem from the background and the separated image undergoes two kinds of processing: one is to skeletonize it using the Zhang–Suen thinning algorithm and the other is to identify the buds and obtain the center coordinates of the buds using the target-detection method YOLOv5; finally, combining with the depth information of the depth image, we use the coordinates of the buds to determine the location of the pruning point located on the skeleton image. The results show that PSP-net has better results in segmentation performance with mIoU reaching 83.73%. YOLOv5 performs better in target detection with mAP reaching 81.06% and F1 reaching 0.80. The accuracy of this method in determining the location of pruning points reaches 82.35%. It can provide a method for fruit tree pruning robots to determine the location of pruning points. [ABSTRACT FROM AUTHOR]

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle. [ABSTRACT FROM AUTHOR]

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network (LBFNet) is proposed in this paper. First, LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996 s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning enables the model accuracy to reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers. [ABSTRACT FROM AUTHOR]

In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated σ0HH and σ0VV) and cross-polarization states (abbreviated σ0HV and σ0VH) and at incidence angles ranging from 24° to 53°. Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4–35.3%), root-mean-square height (range: 0.5–7.9cm) and clay fraction (range: 9–58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters. [ABSTRACT FROM AUTHOR]

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks. [ABSTRACT FROM AUTHOR]

In the current context of the increase in the amount of municipal solid waste as a result of the development of urbanization, in this paper we have analyzed the impact of the use of fly ash obtained from the incineration of municipal solid waste in agriculture on the development of plants as an improver of acidic soils due to the nutrients it contains. We presented ash treatment methods to reduce the content of heavy metals and salts. Based on the studies in the literature, it was found that by adding certain concentrations of fly ash to degraded soils, the quality, porosity, and texture of the soil are improved; the yield of certain crops increases; the water retention capacity of the soil and soil aeration are improved; the density of the soil bulk; the compactness of the soil is reduced; the pH value is optimized; the electrical conductivity of the soil is increased; the crust formation is reduced; and it provides micronutrients to the soil. In the context of the circular economy, by using fly ash as an organic fertilizer in agriculture, the amount of chemical fertilizers harmful to agricultural crops is reduced, the problem of ash storage is solved, and thus it no longer pollutes the environment. [ABSTRACT FROM AUTHOR]

The lack of large balanced datasets in the agricultural field is a glaring problem for researchers and developers to design and train optimal deep learning models. This paper shows that using synthetic data augmentation outperforms the standard methods on object detection models and can be crucially important when datasets are few or imbalanced. The purpose of this study was to synthesize rice leaf disease data using a Style-Generative Adversarial Network Adaptive Discriminator Augmentation (SG2-ADA) and the variance of the Laplacian filter to improve the performance of Faster-Region-Based Convolutional Neural Network (faster-RCNN) and Single Shot Detector (SSD) in detecting the major diseases affecting rice. We collected a few unbalanced raw samples of rice leaf diseases images grouped into four diseases namely; bacterial blight (BB), tungro (TG), brown-spot (BS), and rice-blast (RB) with 1584, 1308, 1440, and 1600 images, respectively. We then train StyleGAN2-ADA for 250 epochs whilst using the variance of the Laplacian filter to discard blurry and poorly generated images. The synthesized images were used for augmenting faster-RCNN and SSD models in detecting rice leaf diseases. The StyleGAN2-ADA model achieved a Fréchet Inception Distance (FID) score of 26.67, Kernel Inception Distance (KID) score of 0.08, Precision of 0.49, and Recall of 0.14. In addition, we attained a mean average precision (mAP) of 0.93 and 0.91 for faster-RCNN and SSD, respectively. The learning curves of loss over 250 epochs are 0.03 and 0.04 for Faster-RCNN and SSD, respectively. In comparison to the standard data augmentation, we achieved a t-test p-value of 9.1 × 10 − 4 and 8.3 × 10 − 5 . Hence, the proposed data augmentation pipeline to improve faster-RCNN and SSD models in detecting rice leaf diseases is significant. Our data augmentation approach is helpful to researchers and developers that are faced with the problem of fewer imbalanced datasets and can also be adopted by other fields faced with the same problems. [ABSTRACT FROM AUTHOR]

In order to solve the labor shortage problem, unmanned agricultural vehicles have been widely promoted in China. Among these vehicles, unmanned tractor-trailers have garnered the most interest thanks to their flexibility and efficiency. However, trapped by the factors of vehicle parameter uncertainties, unstructured farmland roads, etc., the current unmanned tractor-trailers have shown poor tracking accuracy and longer online times in straight-line path tracking. To overcome the aforementioned issues, a fuzzy sliding mode control (FSMC) approach is proposed in this paper. First, the tractor-trailer path tracking error model was established based on the kinematic model and a reference model. Then, according to the sliding mode control (SMC) theory, the FSMC was designed. Through a Lyapunov theory analysis, the proposed control method can ensure that the articulated angle, the position error, and the heading error all converge to zero. Finally, field tests and Simulink simulations demonstrated the effectiveness and robustness of the suggested control mechanism. According to field experiments, the proposed control method can increase the trailer steady-state tracking accuracy by between 10.5 and 36.8% and the tractor steady-state tracking accuracy by between 11.1 and 50%. [ABSTRACT FROM AUTHOR]

Featured Application: Supply delivery tasks are commonly present in hostile regions of natural disasters, in military operations, and in agricultural and urban environments. In this context, the results of this work envision the application of cargo transportation performed by a formation of robots. This paper presents a practical validation of a heterogeneous formation of mobile robots in performing a load lifting, transportation, and delivery task. Assuming that an unmanned ground vehicle (UGV) is unable to perform a mission by itself due to the presence of an obstacle in the navigation route, an unmanned aerial vehicle (UAV) is then assigned to lift the cargo over this UGV, transport the obstacle, and deliver over another UGV. The UAV uses an electromagnetic actuator supported by a cable to pick up the load, the mass of which is 32% of that of the UAV. Experimental results demonstrate that the developed system is capable of performing cargo transport missions and can be scalable for applications such as package delivery in urban or remote areas and supply delivery in conflict or disaster zones. [ABSTRACT FROM AUTHOR]

Color sorting is a technological operation performed with the aim of classifying compliant and noncompliant agricultural products in large-capacity industrial systems for agricultural product processing. This paper investigates the application of the YOLOv3 algorithm on raspberry images as a method developed for the detection, localization, and classification of objects based on convolutional neural networks (CNNs). To our knowledge, this is the first time a YOLO algorithm or CNN has been used with original images from the color sorter to focus on agricultural products. Results of the F1 measure were in the 92–97% range. Images in full resolution, 1024 × 1024, produced an average detection time of 0.37 s. The impact of the hyperparameters that define the YOLOv3 model as well as the impact of the application of the chosen augmentative methods on the model are evaluated. The successful classification of stalks, which is particularly challenging due to their shape, small dimensions, and variations, was achieved. The presented model demonstrates the ability to classify noncompliant products into four classes, some of which are appropriate for reprocessing. The software, including a graphic interface that enables the real-time testing of machine learning algorithm, is developed and presented. [ABSTRACT FROM AUTHOR]

This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29–62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1–87.5% in terms of F1-score and 70.9–87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a smart irrigation context-aware system to be applied on an experimental farm in France. The experimental farm is an ecosystem for agricultural machines and digital information systems developed by an alliance of European organizations and institutes. Moreover, the two ontologies proposed by this paper, CASO and IRRIG, are open access and have the potential to be used by other applications such as smart buildings or smart cities. The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic irrigation overcomes the water shortage problem, and automatic sensor measurements reduce the observational work of farmers. This paper focuses on a method for developing context-aware systems using ontologies. Ontologies are used to solve heterogeneity issues in sensor measurements. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. The context-aware system also contains a decision support system based on a rules inference engine. We propose two new ontologies: The Context-Aware System Ontology addresses the development of the context-aware system in general. The Irrigation ontology automates a manual irrigation method named IRRINOV®. These ontologies reuse well-known ontologies such as the Semantic Sensor Network (SSN) and Smart Appliance REFerence (SAREF). The decision support system uses a set of rules with ontologies to infer daily irrigation decisions for farmers. This project uses real experimental data to evaluate the implementation of the decision support system. [ABSTRACT FROM AUTHOR]

The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of "smart" agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted. [ABSTRACT FROM AUTHOR]

This paper addresses the formulation of an individual fruit harvest decision as a nonlinear programming problem to maximize profit, while considering selective harvesting based on fruit maturity. A model for the operational level decision was developed and includes four features: time window constraints, resource limitations, yield perishability, and uncertainty. The model implementation was demonstrated through numerical studies that compared decisions for different types of worker and analyzed different robotic harvester capabilities for a case study of sweet pepper harvesting. The results show the influence of the maturity classification capabilities of the robot on its output, as well as the improvement in cycle times needed to reach the economic feasibility of a robotic harvester. [ABSTRACT FROM AUTHOR]

Extreme droughts have a strong impact on agricultural production. In France, the 2003 drought generated records of yield losses at a national scale for grassland (more than 30%) and for cereals (more than 10% for soft winter wheat and winter barley). These extreme events raise the question of farm resilience in the future. Studying them makes it possible to adapt risk management policy to climate change. Therefore, the objective of this paper was to analyze the frequency and the intensity of extreme drought in 2050 and their impact on crop yield losses (grassland and cereals) in France. We used the DOWKI (Drought and Overwhelmed Water Key Indicator) meteorological index based on a cumulative water anomaly, which can explain droughts and their consequences on agricultural yield losses at a departmental scale. Then, using the ARPEGE-Climat Model developed by Meteo-France, DOWKI was projected in 2050 and grassland, soft winter wheat, and winter barley yield losses were simulated. The results compare the frequency and intensity of extreme droughts between the climate in 2000 and 2050. Our results show that the frequency of extreme droughts (at least as intense as in 2003) doubled in 2050. In addition, the yield losses due to 10-year droughts increased by 35% for grassland and by more than 70% for cereals. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study. [ABSTRACT FROM AUTHOR]

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies. [ABSTRACT FROM AUTHOR]

The world's population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R 2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions. [ABSTRACT FROM AUTHOR]

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions. [ABSTRACT FROM AUTHOR]

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future. [ABSTRACT FROM AUTHOR]

Featured Application: The featured application of this work is the optimization of breeding techniques for plants susceptible to being damaged by wind. Stem lodging is the bending or breakage of stems in the wind that result in negative economic impacts to producers and processors of small grain crops. To address this issue, plant breeders attempt to quantify lodging using proxy traits such as stem structure and biomechanics. Stem lodging is a function of both stem strength and elasticity. In this paper, we explore the biomechanics of stems approaching the lodging, or permanent bending, condition. Oat, wheat, and two types of barley varying in lodging resistance were exposed to standard growing conditions over the course of a season. Their capability of returning from a bent to unbent state was characterized using a push force meter that measured resistant force and displacement over time. Changes in stem energy and power were then calculated using displacement and force measurements. Lodging susceptibility could be differentiated by stem strength, displacement and change in power measurements depending on small grain species without damaging the plant. These measurements could be used by small cereal grain breeding programs as proxy traits to determine lodging susceptibility without destructively testing or waiting for storm events, thus saving time and resources. [ABSTRACT FROM AUTHOR]

Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs. [ABSTRACT FROM AUTHOR]

Rocchetti et al. [[2]] reversed the classic bottom-up approach of starter cultures to perform an in vitro evaluation of the probiotic properties of microbes that are already commercialized and employed in the dairy sector. In food production, the role of beneficial microorganisms is primarily to give favorable sensory characteristics to food, while also preserving it and imparting functional properties, e.g., probiotic properties. Microorganisms play an important role in human life by influencing agricultural crops, which are raw materials used in the production of food. [Extracted from the article]