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Precision agriculture technologies (PATs) are believed to be able to ensure the sustainability of rice production. However, the adoption of PATs in developing countries is much lower than in developed countries. The basic question of our research is how Indonesian rice farmers are ready to adopt precision agriculture since they are smallholder farmers. Data was collected from 521 rice farmers in five Indonesian provinces, i.e. North Sumatra, West Java, Yogyakarta, South Sulawesi, and East Nusa Tenggara, in 2023. Farmers were interviewed face to face using structured questionnaires. The data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) through the Python software. The results showed that Indonesian rice farmers have a moderate level of readiness. The mean value of the capabilities and opportunities indicators were 2.54 to 3.8, while the range for the opportunity's indicator is 3.23 to 4.11, larger than the capabilities indicators. The level of precision agriculture implementation on Indonesian rice farmers was significant influenced by management (β = 0.42, t = 7.11, p < 0.05), environment (β = 0.17, t = 3.63, p < 0.05), readiness (β = 0.14, t = 2.51, p < 0.05), and technology (β = 0.10, t = 2.12, p < 0.05), economy (β = 0.09, t = 3.63, p < 0.05), and technology2 (β = -0.072, t = 3.5, p < 0.05). Meanwhile, farmer readiness was significantly influenced by opportunity (β = 0.39, t = 6.64, p < 0.05) and capabilities (β = 0.43, t = 6.82, p < 0.05). This research provides information on the status of human resource capacity in exploiting opportunities for implementing precision agriculture and technical policy advice. The Indonesian government should improve farmers' skills in information technology, Global Positioning Systems (GPS), and sensor technology in agricultural sectors, and facilitate access to technology and resources in order to increase rice farmers' readiness to adopt PATs. For opportunity indicators, however, further research is needed to determine which components require immediate attention for construction or development. [ABSTRACT FROM AUTHOR]

This study focuses on semantic segmentation in crop Opuntia spp. orthomosaics; this is a significant challenge due to the inherent variability in the captured images. Manual measurement of Opuntia spp. vegetation areas can be slow and inefficient, highlighting the need for more advanced and accurate methods. For this reason, we propose to use deep learning techniques to provide a more precise and efficient measurement of the vegetation area. Our research focuses on the unique difficulties posed by segmenting high-resolution images exceeding 2000 pixels, a common problem in generating orthomosaics for agricultural monitoring. The research was carried out on a Opuntia spp. cultivation located in the agricultural region of Tulancingo, Hidalgo, Mexico. The images used in this study were obtained by drones and processed using advanced semantic segmentation architectures, including DeepLabV3+, UNet, and UNet Style Xception. The results offer a comparative analysis of the performance of these architectures in the semantic segmentation of Opuntia spp., thus contributing to the development and improvement of crop analysis techniques based on deep learning. This work sets a precedent for future research applying deep learning techniques in agriculture. [ABSTRACT FROM AUTHOR]

The article discusses the importance of farm entrepreneurship and agribusiness management in the agricultural sector. It highlights the need for farmers and agribusinesses to integrate innovation, sustainability, and effective management into their practices. The article emphasizes the role of entrepreneurial competencies, such as financial management and marketing expertise, in successful farm management. It also explores the impact of human capital innovation, technological advancements, and inclusive and sustainable entrepreneurship on the agricultural industry. The article concludes by emphasizing the importance of equipping farm entrepreneurs with the necessary skills and promoting practices that contribute to resilience, innovation, and community development in agriculture. [Extracted from the article]

Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness of pitaya by humans is inefficient, it is therefore of the utmost importance to utilize precision agriculture and smart farming technologies in order to accurately identify the ripeness of pitaya fruit. In order to achieve rapid recognition of pitaya targets in natural environments, we focus on pitaya maturity as the research object. During the growth process, pitaya undergoes changes in its shape and color, with each stage exhibiting significant characteristics. Therefore, we divided the pitaya into four stages according to different maturity levels, namely Bud, Immature, Semi-mature and Mature, and we have designed a lightweight detection and classification network for recognizing the maturity of pitaya fruit based on the YOLOv8n algorithm, namely GSE-YOLO (GhostConv SPPELAN-EMA-YOLO). The specific methods include replacing the convolutional layer of the backbone network in the YOLOv8n model, incorporating attention mechanisms, modifying the loss function, and implementing data augmentation. Our improved YOLOv8n model achieved a detection and recognition accuracy of 85.2%, a recall rate of 87.3%, an F1 score of 86.23, and an mAP50 of 90.9%, addressing the issue of false or missed detection of pitaya ripeness in intricate environments. The experimental results demonstrate that our enhanced YOLOv8n model has attained a commendable level of accuracy in discerning pitaya ripeness, which has a positive impact on the advancement of precision agriculture and smart farming technologies. [ABSTRACT FROM AUTHOR]

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometerbased sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in global food production. Nevertheless, the concern about its impact on the environment and sustainability is increasing. This article addresses modern technologies that are used or can be used in sustainable agriculture. There are discussions of various technological innovations, including precision agriculture and remote sensing. These innovations have demonstrated the prospects of reducing resource use, increasing efficiency, and improving environmental sustainability in agriculture. The authors also highlighted the problems and limitations of these technologies, such as cost, availability, and possible unintended consequences. In addition, the article discusses the need for interdisciplinary cooperation between researchers, politicians, and farmers to ensure the successful implementation of these technologies in agriculture. In general, the findings emphasize the need for the introduction of technologies in agriculture for sustainable methods and the potential of technologies to contribute to sustainable agriculture in the future. [ABSTRACT FROM AUTHOR]

Cellular agriculture is an innovative technology for manufacturing sustainable agricultural products as an alternative to traditional agriculture. While most cellular agriculture is predominantly centered on the production of cultured meat, there is a growing demand for an understanding of the production techniques involved in dairy products within cellular agriculture. This review focuses on the current status of cellular agriculture in the dairy sector and technical challenges for cell-cultured milk production. Cellular agriculture technology in the dairy sector has been classified into fermentation-based and animal cell culture-based cellular agriculture. Currently, various companies synthesize milk components through precision fermentation technology. Nevertheless, several startup companies are pursuing animal cell-based technology, driven by public concerns regarding genetically modified organisms in precision fermentation technology. Hence, this review offers an up-to-date exploration of animal cell-based cellular agriculture to produce milk components, specifically emphasizing the structural, functional, and productive aspects of mammary epithelial cells, providing new information for industry and academia. [ABSTRACT FROM AUTHOR]

Addressing within-field spatial variability for nitrogen (N) management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study, we investigated the economic, environmental, and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum L.). An online visible and near-infrared (vis-NIR) spectroscopy sensor was utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop's normalized difference vegetation index (NDVI) from Sentinel-2 satellite images was incorporated into online measured soil data to derive fertility management zones (MZs) maps after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium–high fertile (VR-MH), medium–low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. The results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR-N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment is 374.83 EUR/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific applications of all farm resources through precision agricultural technologies. [ABSTRACT FROM AUTHOR]

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University's (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm's processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards. [ABSTRACT FROM AUTHOR]

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management. [ABSTRACT FROM AUTHOR]

The digitization of agriculture is widely discussed today. But despite proven benefits, its acceptance in agricultural practice remains low. In small-structured areas, this trend is even more pronounced. There are even known cases where farmers initially purchased and used technology, but then stopped using it due to lack of profitability or other reasons. Interestingly, despite extensive research on precision agriculture technologies (PATs), the processes of adoption and phase-out with their associated economic impacts have never been studied. This paper provides a methodological framework for evaluating the economics of PAT deployment, taking into account changes during the period of use; the framework provides decision rules for determining the appropriate time to phase out technology. Using a selected PAT, a farm model, and defined entry and exit scenarios, it was shown that farms with outdated technology and farms with retrofittable technology are at a significant economic disadvantage during implementation compared to farms already using technology suitable for site-specific fertilization or farms relying on the use of a contractor. And even in the event of a phase-out, the two disadvantaged starting conditions face significantly greater uncertainties and costs. Moreover, the decision to phase out in time is difficult, as making an informed and fact-based decision is not possible after the first year of use. Therefore, it is advisable that farmers are not only accompanied before and during phase-in, but also receive professional support during use. [ABSTRACT FROM AUTHOR]

Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impacts. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep-learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image–mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder–decoder model architecture that uses both synthesized image–mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7% on an internal test dataset and a Dice score of 64.8% on an external test set composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture. [ABSTRACT FROM AUTHOR]

In contemporary agriculture, researchers are focusing on precision farming and environmentally conscious techniques, leading to the advancement of autonomous agricultural mobile robots. Consequently, A navigation system using TSK Interval Type-2 fuzzy logic has been developed and, for the first time, applied to a holonomic mobile agricultural robot equipped with four-wheel drive and steer. This system employs a behavioral approach to accomplish the robot's primary objective in unfamiliar environments. It comprises four controllers: one exclusively dedicated to goal-seeking behavior, with the remaining controllers responsible for obstacle avoidance. Supervision of these controllers is conducted through the Mandani fuzzy logic approach, which combines the outputs of all four controllers, robot wheel velocities and steering angles, using a proposed equations. Various case studies were examined and simulated utilizing MATLAB and VREP software platform to assess the navigator system's performance. The simulation outcomes showcase the efficiency of the proposed equations and the system's navigation capabilities. The stability of the entire system was thoroughly examined and confirmed, employing the Lyapunov function, in environments with and without obstacles. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices. [ABSTRACT FROM AUTHOR]

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

This paper discusses the role of technology and big data in the agri-food industry. With the world population growing rapidly, there is a need to produce more food to feed everyone. Agriculture is changing rapidly, and technology is playing a crucial role in this change. The concept of Agriculture 4.0 seeks to optimize every factor to minimize inputs and maximize efficiency. Big data is being collected to improve management and farm management. Precision farming is being used to predict crop development phases and yields. The European Union is lagging behind the US and China in terms of innovation and market evaluation. Innovation drives economic growth, and it goes beyond invention. Small and medium-sized enterprises are playing an essential role in driving economic growth, particularly in the agri-food business. To improve resilience and production efficiency in the agri-food business, there is a need to boost SMEs' competitiveness. The European agri-food business is lagging in terms of digitalization, but there is a significant potential for growth. Finally, technology and big data are playing a crucial role in the agri-food industry, and there is a need to evaluate contextual elements to improve farmers' technology use. [ABSTRACT FROM AUTHOR]

Improving agricultural production relies on the decisions and actions of farmers and land managers, highlighting the importance of efficient soil monitoring techniques for better resource management and reduced environmental impacts. Despite considerable advancements in soil sensors, their traditional bulky counterparts cause difficulty in widespread adoption and large-scale deployment. Printed electronics emerge as a promising technology, offering flexibility in device design, cost-effectiveness for mass production, and a compact footprint suitable for versatile deployment platforms. This review overviews how printed sensors are used in monitoring soil parameters through electrochemical sensing mechanisms, enabling direct measurement of nutrients, moisture content, pH value, and others. Notably, printed sensors address scalability and cost concerns in fabrication, making them suitable for deployment across large crop fields. Additionally, seamlessly integrating printed sensors with printed antenna units or traditional integrated circuits can facilitate comprehensive functionality for real-time data collection and communication. This real-time information empowers informed decision-making, optimizes resource management, and enhances crop yield. This review aims to provide a comprehensive overview of recent work related to printed electrochemical soil sensors, ultimately providing insight into future research directions that can enable widespread adoption of precision agriculture technologies. [ABSTRACT FROM AUTHOR]

In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning. Core Ideas: There are concerns that decision‐making tools using machine learning and artificial intelligence (ML/AI) in farming could replace human knowledge and labor.ML/AI technologies have the potential to improve precision farming using site‐specific algorithms.This special issue aims to open a dialogue about the role of ML/AI in agriculture and identify inequalities that may result from its use. [ABSTRACT FROM AUTHOR]

Climate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing IoT; developing energy‐efficient and affordable technologies; and improving data collection, transfer, storage, processing, security, anonymity, and privacy. This paper further calls for collaborative research in sensor and communication technologies in the agricultural sector. Core Ideas: Different platforms, sensors, and structures of IoT in digital agriculture were reviewed.The application of IoT in data management, interoperability, and precision farming was reviewed.Potential of IoT to improve agricultural quality and productivity was highlighted.Challenges and opportunities of IoT in digital agriculture were discussed. [ABSTRACT FROM AUTHOR]

The use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers' concerns about DSS. Factor loadings indicated that farmers' concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States. Core Ideas: Study seeks to understand famers' engagement and concerns with machine learning‐based agricultural decision support systems (DSS).Farmers' concerns about DSS were categorized into four dimensions—security, knowledge, cost, and confidence.The concern profiles allow for more inclusive and targeted engagement with farmers and may increase DSS adoption. [ABSTRACT FROM AUTHOR]

This study delves into the adoption of Internet of Things (IoT) technology in the agriculture industry, which has the potential to revolutionize resource allocation and operational efficiency and meet the growing global demand for food and water. Despite its potential, the adoption of IoT technology in agriculture remains generally low. Hence, this research aims to explore the motivating factors influencing IoT adoption, assess the impacts of adoption on agriculturists, and identify the challenges that need to be addressed to promote future IoT adoption in the agricultural sector. Semi-structured interviews with Norwegian agriculturists who have adopted IoT technologies were conducted using an exploratory qualitative approach. Our findings reveal several key factors that influence IoT adoption, including the availability of technical support, governmental policies, and the intentions of agriculturists' children to carry on farming traditions. The adoption of IoT has led to improved decision-making, operational efficiency, and control for agriculturists, although they may not be fully aware of all the consequences associated with adoption. Additionally, the study highlights a lack of support and knowledge among industry stakeholders, which could hinder the diffusion of IoT adoption in agriculture. These findings contribute to our understanding of the complexities surrounding IoT adoption in agriculture and provide insights for policymakers, industry stakeholders, and agriculturists seeking to maximize the benefits of IoT technology. Addressing the challenges identified, such as enhancing technical support systems, developing supportive policies, and increasing awareness among agriculturists, can foster a conducive environment for greater IoT adoption in agriculture in Norway. [ABSTRACT FROM AUTHOR]

Machine learning, a subset of artificial intelligence, is revolutionizing agriculture by enabling enhanced crop monitoring, disease detection, and yield prediction. Its application extends to precision farming, where it aids in optimizing irrigation, fertilization, and harvesting by analyzing large datasets from mobile phones, sensors and satellite images. It has played a crucial role in identifying and categorizing different types of plants. Specifically, Convolutional Neural Networks (CNN), a deep learning technology within ML, are employed to classify plant species based on images. In the present research work, we have employed machine learning for the classification of citrus species using Convolutional Neural Networks (CNNs). A dataset has been developed with images of fruits and leaves from ten citrus species. We have utilized transfer learning with architectures like Mobile Net, Alex Net, and Goog Le Net. The study demonstrates that combining multiple plant components in CNN analysis improves classification accuracy, with leaves providing more reliable results than fruits. This approach signifies a major advancement in agricultural technology, allowing for more precise and efficient farming practices. The findings indicate that expanding the dataset and incorporating more plant structures could further refine these models. This research highlights the potential of machine learning in agriculture, particularly in enhancing species classification, which is crucial for sustainable and productive farming. [ABSTRACT FROM AUTHOR]

On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize (Zea mays L.) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error < 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in > 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54). [ABSTRACT FROM AUTHOR]

Sustainable intensification (SI) responds to the concurrent challenges of increasing food production while reducing the environmental impacts of agriculture. As an early disclosure of innovation, patents are a useful indicator of technology market potential. However, we lack understanding of the extent to which current agricultural technology patents relate to the goals of SI and which kinds of technologies can potentially address SI. Here, we analyzed the diffusion and focus of more than one million patents issued during the period 1970–2022. We explored the degree to which the patents relate to SI through the co-occurrence of efficiency and environmental friendliness targets. Our results reveal that while the rate of patent issuance has dramatically increased over the past five decades, the rate at which patents diffused to different countries had decreased over time. The USA was the biggest net exporter of patents and had produced by far the most high-impact patents (in the top 1% most-cited patents). Since 1970, only 4% of agricultural patents and 6% of high-impact patents were related to SI targets (i.e., promoting both agricultural efficiency and environmental friendliness), but the attention to SI has increased over time. The most highly cited SI-related patents had become more diverse over time, shifting from digital, machine, and energy technologies in 1980s to the current era of agroecology, information, and computer networking. Our results provide an early indication of promising technologies that may play a greater role for SI in the future, subject to the challenges of market transfer and farm adoption and complemented by non-technological innovations in farm management and institutional support. [ABSTRACT FROM AUTHOR]

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This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security. [ABSTRACT FROM AUTHOR]

The integration of agricultural robots in precision farming plays a pivotal role in tackling the pressing demands of minimizing energy usage, enhancing productivity, and maximizing crop yield to meet the needs of an expanding global population and depleting non-renewable resources. Evaluating the energy expenditure is vital when assessing agricultural machinery systems. Through the reduction of fuel consumption, operational costs can be curtailed while simultaneously minimizing the overall environmental footprint left by these machines. Accurately calculating fuel usage empowers farmers to make well-informed decisions about their farming operations, resulting in more sustainable and productive methods. In this study, the ASABE model was applied to predict the fuel consumption of the studied robot. Results show that the ASABE model can predict the fuel consumption of the robot with an average error equal to 27.5%. Moreover, different machine-learning techniques were applied to develop an effective and novel model for estimating the fuel consumption of an agricultural robot. The proposed GPR model (gaussian process regression) considers four operational features of the studied robot: total operational time, total traveled distance, automatic working distance, and automatic turning distance. The GPR model with four features, considering hyperparameter optimization, showed the best performance (R-squared validation = 0.93, R-squared test = 1.00) among other models. Furthermore, three different ML methods (gradient boosting, random forest, and XGBoost) were considered in this study and compared with the developed GPR model. The results show that the GPR model outperformed the mentioned models. Moreover, the one-way ANOVA test results revealed that the predicted values from the GPR model and observation do not have significantly different means. The results of the sensitivity analysis show that the traveled distance and the total time have a significant correlation with the fuel consumption of the studied robot. [ABSTRACT FROM AUTHOR]

This review article examines the potential for intensifying Russian crop production through digital transformation, particularly through the use of unmanned aerial vehicles (UAVs). (1) The importance of this topic is driven by declining food security in some parts of the world and the Russian government's goal to increase grain exports by 2050. (2) Comparisons of agriculture technologies suggest that the use of UAVs for crop treatment with agrochemicals is economically effective in certain cases. (3) Specifically, UAV treatment is advantageous for plots with irregular shapes, larger than 2 ha, and containing between 9 and 19% infertile land. It is also important to agree on the flight parameters of the UAV, such as speed and altitude, as well as the type of on-board sprayer and agrochemical. In case of insufficient funds or expertise, it is recommended to hire specialized companies. (4) The listed peculiarities of Russian crop production led to assumptions about the regions where the use of UAVs for agrochemical treatment of crops would be economically effective. [ABSTRACT FROM AUTHOR]

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, "smart" technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. "Smart Agriculture/farming" (SA) or "Digital Agriculture" (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA's ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users' facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language. [ABSTRACT FROM AUTHOR]

This paper describes approaches to improving the efficiency of agricultural enterprises. It has been established that the use of technology transfer, in particular, precision farming technologies, makes it possible to enhance the efficiency of agricultural enterprises, in particular, to increase the yield of agricultural crops and, accordingly, to improve the profitability of enterprises. As an example, the activities of agricultural enterprises in the Republic of Kazakhstan were analyzed. The share of agricultural enterprises in the Republic of Kazakhstan that use elements of precision farming technology in their activities was determined. It was established that since 2019, the use of precision farming technologies in the activities of agricultural enterprises in the Republic of Kazakhstan has intensified. At the same time, from 60 to 75 % of enterprises already use elements of precision farming technologies in their activities. Using crop yield data for the past 32 years, estimates of the effectiveness of using precision farming technologies by agro-enterprises in the Republic of Kazakhstan were constructed based on forecasting yield indicators using the linear-weighted moving average method. The efficiency of using precision farming technologies in the activities of agricultural enterprises in the Republic of Kazakhstan in 2022 reached 8.46 %, and the average efficiency for the period 2019–2022 was 4.21 %. Therefore, the use of precision farming technologies makes it possible to improve the validity of decision-making in the management of an agricultural enterprise and to obtain a higher profit from the sale of produced agricultural products for any agricultural enterprise in the world. On average, the results allow us to estimate the possible profit of an agro-enterprise when growing agricultural crops in the case of using precision farming technology. [ABSTRACT FROM AUTHOR]

Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf's relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phytopathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10–16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows an aptitude for automated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models integrating weather, soil, and crop data enable preemptive interventions by predicting spatial-temporal outbreak risks weeks in advance at 81–95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI's proficiency in illuminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correlations unnoticed before. AI is poised to transform in-field decision-making around disease prevention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agricultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide. [ABSTRACT FROM AUTHOR]

The study showcases the process of deep learning operated in agriculture, including Deep IoT, which makes the procedure easier using the deep neural system. The use of the IoT in the agrarian sectors makes the evolution of firms more effective. The application of the IoT detector supports the making of grade derivatives in the husbandry department. Marketing of crop finance is two other operations of smart agriculture that help for better harvest farming. Through the IoT technology in the farming industry, agriculturalists can get notifications about the temperature and climate. The method needs professional and qualified employees in the division to properly monitor the system and the methods. The submission of the proper nourishment for the proper crop increases the life duration of the harvest and makes the crop free from menace. The velocity of the manufacture of undeveloped items can also be improved by using the IoT. The function of the BDA and IoT has enlarged for the healthier construction of farming items. The foreword of elegant farming in the rural industry requires more capable and qualified trainers to give the personnel proper teaching. The urbanization of the farming process and the use of elegant and modern technology are well-designed in the time of "Agriculture 3.0." [ABSTRACT FROM AUTHOR]

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers. [ABSTRACT FROM AUTHOR]

The uniform management used in conventional agriculture is not the most efficient strategy for managing agricultural areas. In this way, precision agriculture (PA), based on assessments of the variability of chemical, physical, and biological attributes, seeks to optimize the activities management by site-specific management of inputs, to increase profitability in the production system, and to minimize environmental impacts. In this context, this work aimed to compare two techniques to make traditional thematic maps (TTMs) and management zones (MZs) for site-specific lime application, using data from three commercial agricultural areas (Western Paraná State, Brazil) and three methods to calculate the lime requirement (LR): (1) neutralization of exchangeable aluminum (Al3+) (NEM), (2) increase of calcium (Ca2+) and magnesium (Mg2+) (KM), and (3) base saturation method (BSM). The LR was calculated on AgDataBox-Map (ADB-Map) web platform, using AgDataBox-Map-Aplic module (ADB-Map-Aplic). ADB-Map is an application of the web platform, which aims at integrating data, software, procedures, and methodologies for Digital Agriculture. The Variance Reduction (VR%), Fuzziness Performance Index (FPI), Modified Partition Entropy (MPE), Improved Cluster Validation Index (ICVI), Smoothness Index (SI%) and Fragmentation index (FI%) indices were used to assess MZs quality, and the Kappa and global accuracy indices compared the agreement between TTMs and MZs. The results showed that the BSM method presented the best results in LR and distribution. According to the indices, MZs delineated with two classes generally (45 of the 47) showed the best results. MZs with application rates in each class calculated by the median of the lime quantities provided the lowest total LR per area. Thus, the MZs delineation allows applying lime more efficiently, reducing production costs, and allowing more sustainable use of inputs. [ABSTRACT FROM AUTHOR]

This article discusses the application of artificial intelligence (AI) in precision and sustainable agriculture. It highlights the importance of agriculture in global economies and its role in eradicating poverty and feeding a growing population. The article emphasizes the need to boost crop production while facing the challenges of climate change. AI, specifically machine learning and data analytics, is presented as a powerful tool for revolutionizing precision agriculture. The integration of AI into farming techniques can improve sustainability, productivity, and resilience in the agricultural sector. The article also features five unique research articles that focus on applying AI-based algorithms to solve problems in smart and precision farming, such as fruit-picking automation, pear sorting, jujube fruit identification, mechanized harvesting for hydroponic lettuces, and analyzing the effects of climate change on soybean yields. Overall, the use of AI in precision farming allows for knowledgeable decision-making, optimization of resource allocation, real-time crop monitoring, efficient fruit picking and harvesting, and accurate yield prediction. This synergy promotes sustainable practices and enables farmers to meet the growing demands of agriculture. [Extracted from the article]

Despite a recent narrowing of the digital divide, there remains a lag in adopting Precision Agriculture Technology (PAT). Based on structuration theory and diffusion of innovations theory, the present study uses semi-structured interviews with forty board members from two U.S. national agricultural boards to determine how internet access impacts farming leaders' knowledge and perceptions of farming PAT innovations. Farming leaders' responses suggest a lack of competition among internet service providers within areas, a reliance on cellular data to complete farming operations, limits to available internet infrastructure, and low perceived technology knowledge. Farming leaders, especially those with high perceived knowledge, noted using various farming technologies that assisted with business operations, entertainment, and social communications. Overall, many participants were willing to adopt more farming technology if they had better internet services and more technological knowledge. Farming PAT diffusion is limited by a lack of access to resources, such as knowledge, workforce, and infrastructure, even among those who tend to be more resource-rich, like farming leaders. These findings highlight how a lack of resources limits a farmer's ability to express agency over the technology they adopt and, by extension, their business operations. These findings have implications for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Innovations in precision agriculture continue to increase in both number and significance. Contemporaneously, given their expanding scope and application, the growing pool of technologies is increasingly complex. Although some recent attempts have been made, a knowledge gap remains in respect to the evolution of precision agricultural innovation. Synthesized, such knowledge should inform the untapped opportunities for innovation. This paper narrows this gap by conducting a network analysis of patent applications in areas concerning precision agriculture. Spanning the years from 1960 to 2021, the analysis shows an annual average growth in patent applications of 15.1%. During the last decade, this growth is almost entirely attributable to China and the USA. Priority innovation themes have been dynamic. Its phases can be characterized as 'motorized mechanization' (1960–1999), 'mechanical automation' (2000–2009), 'digital mechanization' (2010–2019), and 'digital automation' (2020–2021). The significance of information communication technology has grown in association with other innovation clusters, but a lag time has persisted in introducing innovations based on such technologies. Opportunities are ripe for speeding transference through economic and other incentives. [ABSTRACT FROM AUTHOR]

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation. [ABSTRACT FROM AUTHOR]

Automation and robotics are the key players in modern agriculture. They offer potential solutions for challenges related to the growing global population, demographic shifts, and economic status. This review paper evaluates the challenges and opportunities of using new technologies and the often-missed link between automation technology and agricultural economics. Through a systematic analysis of the literature, this study explores the potential of automation and robotics in farming practices, as well as their socio-economic effects, and provides strategic recommendations for those involved. For this purpose, various types of robots in different fields of agriculture and the technical feasibility and challenges of using automation have been discussed. Other important factors, including demographic shifts, labor market effects, and economic considerations, have been analyzed. Furthermore, this study investigates the social effects of automation, particularly in terms of employment and workforce adaptation. It finds that, while automation boosts productivity and sustainability, it also causes labor displacement and demands considerable technological investment. This thorough investigation fills a crucial gap by assessing economic sustainability, labor market evolution, and the future of precision agriculture. It also charts a course for further research and policy-making at the intersection of agricultural technology and socio-economic fields and outlines a future roadmap for further research and policy. [ABSTRACT FROM AUTHOR]

The evolution of agriculture towards a modern, intelligent system is crucial for achieving sustainable development and ensuring food security. In this context, leveraging the Internet of Things (IoT) stands as a pivotal strategy to enhance both crop quantity and quality while effectively managing natural resources such as water and fertilizer. Wireless sensor networks, the backbone of IoT-based smart agricultural infrastructure, gather ecosystem data and transmit them to sinks and drones. However, challenges persist, notably in network connectivity, energy consumption, and network lifetime, particularly when facing supernode and relay node failures. This paper introduces an innovative approach to address these challenges within heterogeneous wireless sensor network-based smart agriculture. The proposed solution comprises a novel connectivity management scheme and a dynamic clustering method facilitated by five distributed algorithms. The first and second algorithms focus on path collection, establishing connections between each node and m-supernodes via k-disjoint paths to ensure network robustness. The third and fourth algorithms provide sustained network connectivity during node and supernode failures by adjusting transmission powers and dynamically clustering agriculture sensors based on residual energy. In the fifth algorithm, an optimization algorithm is implemented on the dominating set problem to strategically position a subset of relay nodes as migration points for mobile supernodes to balance the network's energy depletion. The suggested solution demonstrates superior performance in addressing connectivity, failure tolerance, load balancing, and network lifetime, ensuring optimal agricultural outcomes. [ABSTRACT FROM AUTHOR]

The agricultural sector plays an important role in contributing significantly to the gross domestic product (GDP) growth in developing countries. On the other hand, agriculture is widely affected by major factors such as environmental changes, natural disasters, pesticide control, and soil and irrigation-related issues, which reduce crop yield. The convergence of Industry 4.0 and agriculture offers an opportunity to move into the next generation of Agriculture 4.0. The internet of things (IoT), remote sensing, machine learning, deep learning, big data, cloud computing, thermal imaging, end-user apps and unmanned aerial vehicles offer a full-stack solution. IoT provides the ubiquitous connectivity of smart devices to the internet to collect, process and analyse a large amount of agriculture field data more quickly and synthesize them to make smart decisions using various machine learning and deep learning algorithms. This study reviews the challenges and major issues in the IoT agriculture domain and explores its emergence with new technologies. It covers the existing literature and illustrates how IoT applicationbased precision agriculture solutions have contributed. A case study on weed detection for smart agriculture using the YOLOv5 model is presented, achieving high accuracy. Finally, various IoT agriculture use cases are discussed, along with current research issues and possible solutions for future IoT-based agriculture advancement. [ABSTRACT FROM AUTHOR]

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem. [ABSTRACT FROM AUTHOR]

In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps. [ABSTRACT FROM AUTHOR]

Digital transformation in manufacturing necessitates the integration of advanced fields such as robotics, the Internet of Things (IoT), and data science. The latter two, in particular, introduce a unique set of challenges related to data acquisition and processing, especially for emerging manufacturing sectors that lack substantial data volumes for analytical software. This paper specifically addresses the challenges associated with limited data within the context of precision poultry farming. We begin by identifying the specific challenges encountered during the development of a smart poultry management software system. Subsequently, we propose potential solutions for each identified challenge. The outcome of this study is a comprehensive algorithm for data processing that encapsulates these proposed solutions, offering a practical approach to overcoming data limitations in precision poultry farming. [ABSTRACT FROM AUTHOR]