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As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of the baler. Via theoretical analysis, GA—BP (Genetic Algorithm—Back Propagation) simulation, and comparative experiments, the structural parameters and rotational speed of the spiral shaft in the screw conveying device are optimized. In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw's conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. In MATLAB, an optimization calculation and analysis were performed. The optimization results of the traditional BP (Back Propagation) and GA—BP were compared. It was proven that GA—BP could effectively compensate for the deficiencies of the BP neural network and substantially enhance the model's accuracy. Through an analysis of the optimization results, the conclusion of attaining the optimization objective was drawn. Specifically, when the outer diameter of the spiral for screw conveyance in the straw baler was D = 320   mm , the pitch was S = 200   mm , and the rotational speed of the pickup shaft was n = 138   r / min , the straw baler could achieve the maximum conveying capacity and the minimum power consumption. At this moment, the power consumption was P = 0.079   kW , and the conveying capacity was Q m = 23.98   t / h . Subsequently, the optimization results were contrasted with those of other mainstream domestic models, and a comparative experiment was conducted. The experimental results indicated that the model's prediction results were reliable and exhibited higher efficiency compared to other combinations. The results could provide a reference for the research on screw conveyance of balers. [ABSTRACT FROM AUTHOR]

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed. [ABSTRACT FROM AUTHOR]

The present paper aims to quantify the carbon emissions associated with the establishment of 15 walnut orchards ("Juglans californica") in the greater area of Magnisia, Greece, with the use of a carbon footprint tool interconnected to a Farm Management Information System. The data collection spanned the first five years following the planting of the trees, providing a comprehensive view of the emissions during this critical establishment phase. Over the five-year period examined (February 2019–December 2023), the results revealed net carbon emissions amounting to 13.71 tn CO2 eq ha−1, with the calculated emissions showing an increasing trend from the first year through the fifth year. Scope 1 (7.38 tn CO2 eq ha−1) and Scope 2 (3.71 tn CO2 eq ha−1) emissions emerged as the most significant, while irrigation (drip irrigation) and fertilizing practices were identified as the highest contributors to emissions. This study highlights the significance of using integrated digital tools for monitoring the performance of cultivations rather than standalone tools that are currently widely available. Integrated tools that incorporate various applications simplify data collection, encourage accurate record-keeping, and facilitate certification processes. By automating data entry and calculations, these tools reduce human error during agricultural carbon management and save time; thus, the integration of digital monitoring tools is vital in improving data accuracy, streamlining certification processes, and promoting eco-friendly practices, crucial for the evolving carbon market. [ABSTRACT FROM AUTHOR]

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such noninvasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods. [ABSTRACT FROM AUTHOR]

The concept of digital farming can help farmers increase their agricultural production yield. One of the technologies to support digital farming is robotics, which can be utilized to complete a redundant task efficiently for 24 hours. This paper presents a simple and effective harvesting robot that is applied to harvest a ripe strawberry. The mechanical and electrical design is kept simple to ensure it is reproducible. The input from a proximity sensor and image detection by a Pi camera is utilized by FLC (Fuzzy Logic Controller) to improve the effectiveness of the harvesting task. The image processing method in this study is image segmentation, which fits with the limited source of the microcontroller available in the market. The experiment included 60 times (20 times center, left, and right position) harvesting using the FLC algorithm and 60 times without FLC to show the effectiveness of the proposed method. From 60 experiments without an FLC experiment, there is an 80% hit rate for strawberries positioned in the middle of an image plane and 55% for left and right strawberries. From 60 times of FLC experiment, 95% hit rate for strawberries positioned in the middle of an image plane, 80% for left and right strawberries. The average time required to finish the task without FLC for strawberries in the middle is 13.51 s, the left is 11.04 s, and the right is 17.28 s. While the average time required to finish the task with FLC for strawberry in the middle is 12.90 s, the left side is 11.71 s, and the right side is 10.93 s. This study is intended to show that simple designs can be helpful and affordable when applied to greenhouse farming in Indonesia.

In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users’ experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm.

With the intensified use and integration of digital technologies in agriculture, dependencies and constraints occurred which weakened the adoption and reduced effectiveness of innovative technology due to lacking interoperability and resilience. As awareness of these problems increased concepts have been developed to meet this issue with decentralized IT- infrastructures. With the proposed concept the authors aim to refine these existing infrastructures with concrete suggestions for server infrastructures. Off-the-shelf hardware and open-source software, enable cheap access to digital technologies yet provide sufficient support by choosing open-source tools with big or active communities. With the involvement of the machinery rings the economic advantages scale up because of the interfarm use of expensive technology. The farmservers on the farmside are the edge nodes of a regional network. The local machinery ring is the next node which is supposed to offer remote services for the farmers, who have a trustful partner in the machinery rings. The concept orients on revised requirements enriched by the results of a survey, conducted by the authors, adding the focus on interfarm cooperations. The concept meets the main constraints farmers face in digitalization: Data sovereignty, resilience, interoperability, high costs, and trust.

BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability. RESULTS: This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine‐learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models. CONCLUSION: These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The estimation of pre-harvest fruit quality and maturity is essential for growers to determine the harvest timing, storage requirements and profitability of the crop yield. In-field fruit maturity indicators are highly variable and require high spatiotemporal resolution data, which can be obtained from contemporary precision agriculture systems. Such systems exploit various state-of-the-art sensors, increasingly relying on spectrometry and imaging techniques in association with advanced Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. This article presents a critical review of precision agriculture techniques for fruit maturity estimation, with a focus on destructive and non-destructive measurement approaches, and the applications of ML in the domain. A critical analysis of the advantages and disadvantages of different techniques is conducted by surveying recent articles on non-destructive methods to discern trends in performance and applicability. Advanced data-fusion methods for combining information from multiple non-destructive sensors are increasingly being used to develop more accurate representations of fruit maturity for the entire field. This is achieved by incorporating AI algorithms, such as support vector machines, k-nearest neighbour, neural networks, and clustering. Based on an extensive survey of recently published research, the review also identifies the most effective fruit maturity indices, namely: sugar content, acidity and firmness. The review concludes by highlighting the outstanding technical challenges and identifies the most promising areas for future research. Hence, this research has the potential to provide a valuable resource for the growers, allowing them to familiarize themselves with contemporary Smart Agricultural methodologies currently in use. These practices can be gradually incorporated from their perspective, taking into account the availability of non-destructive techniques and the use of efficient fruit maturity indices. [ABSTRACT FROM AUTHOR]

In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users' experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm. [ABSTRACT FROM AUTHOR]

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this “smart agriculture” can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

As technology advances, so does digital farming, revolutionizing the industry. Drones, sprayers equipped with GPS and other sensors, combine harvesters, and other machinery can greatly improve agricultural productivity. This paper studies the impact of the straw baler screw conveyor on the efficiency of the baler. Via theoretical analysis, GA—BP (Genetic Algorithm—Back Propagation) simulation, and comparative experiments, the structural parameters and rotational speed of the spiral shaft in the screw conveying device are optimized. In this paper, we analyze the force and velocity components acting on the straw, give the design principles for the screw’s conveying parameters under the premise of ensuring maximum conveying capacity and minimum power consumption, and determine the optimal design variables, objective functions, and constraints according to the specific optimization problem; we establish a specific mathematical model, and introduce algorithm optimization for nonlinear problems with many variables and large amounts of calculations. In MATLAB, an optimization calculation and analysis were performed. The optimization results of the traditional BP (Back Propagation) and GA—BP were compared. It was proven that GA—BP could effectively compensate for the deficiencies of the BP neural network and substantially enhance the model’s accuracy. Through an analysis of the optimization results, the conclusion of attaining the optimization objective was drawn. Specifically, when the outer diameter of the spiral for screw conveyance in the straw baler was D=320 mm, the pitch was S=200 mm, and the rotational speed of the pickup shaft was n=138 r/min, the straw baler could achieve the maximum conveying capacity and the minimum power consumption. At this moment, the power consumption was P=0.079 kW, and the conveying capacity was Qm=23.98 t/h. Subsequently, the optimization results were contrasted with those of other mainstream domestic models, and a comparative experiment was conducted. The experimental results indicated that the model’s prediction results were reliable and exhibited higher efficiency compared to other combinations. The results could provide a reference for the research on screw conveyance of balers.

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets.

The present rice cultivation systems face challenges of low production, water scarcity, shrinking cultivable land area due to degradation and urbanization, labour shortage, diminishing soil health, climate change, greenhouse gas emissions and low income for farmers. Changes and/or modifications are thus necessitated in rice production to feed future generations. The aim of next-gen rice farming is to provide food, nutrition and economic security, as well as climate-smart solutions to safeguard ecosystems while using better tools and techniques, improved cultivars and management practices. To achieve these, there is a need to develop suitable farm mechanization for small-sized fields, precision (sensor-based) water-saving irrigation methods, greater input use-efficient systems, digital farming considering soil health improvement and proper utilization of rice straw. Next-gen rice farming should be taken as a business opportunity for the youth to earn more income and must be supported by a favourable Government policy. [ABSTRACT FROM AUTHOR]

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle, just like traditional agriculture. The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system. Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security, reduce water scarcity, and eliminate carbon footprint. For the large-scale implementation of this farming technique, a unified system is needed that can detect crop diseases and support researchers and farmers in identifying potential causes and treatments at early stages. This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops, lettuce, basil, spinach, and parsley, grown in an aquaponics facility. First, a dataset comprising 2640 images is constructed. Then, a disease detection system is developed that works in three phases. The first phase is a crop classification system that identifies the type of crop. The second phase is a disease identification system that determines the crop's health status. The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease. The proposed approach has shown promising results with accuracy in each of the three phases, reaching 95.83%, 94.13%, and 82.13%, respectively. The final disease detection system is then integrated with an ontology model through a cloud-based application. This ontology model contains domain knowledge related to crop pathology, particularly causes and treatments of different diseases of the studied leafy green crops, which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures. The proposed application finds its significance as a decision support system that can automate aquaponics facility health monitoring and assist agricultural practitioners in decision-making processes regarding crop and disease management.

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed.

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated into modern farming systems, such as aquaponics, to address the challenges hindering its commercialization and large-scale implementation. Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture, that promises to address food security issues. To complement the current research efforts, a methodology is proposed to automatically measure the morphological traits of crops such as width, length and area and estimate the effective plant spacing between grow channels. Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows. The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm. For common little gem romaine lettuce, the growth is estimated within 2 cm of error for both length and width. The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system. The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions. The proposed application finds its significance as a decision support system that can pave the way for intelligent system monitoring and control.

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. [ABSTRACT FROM AUTHOR]

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. [ABSTRACT FROM AUTHOR]

Agriculture needs to reduce its current dependence toward pesticides while reducing crop losses caused by pests and ensuring food security; Integrated Pest Management (IPM) is considered the most appropriate approach to achieve the goal. More specifically, growers should use tools that enable informed decisions on whether and when crop protection is needed, and which methods should be used. These tools include risk algorithms, decision rules, intervention thresholds, and decision support systems (DSSs), collectively named decision tools (DTs). A large number of DTs have been developed and made available to advisors and farmers, mainly through Internet-based systems. The adoption rate of these systems, however, has been low because of technical limitations and farmer perceptions. Fragmentation of the DTs offered, poor local implementation, and restriction to particular users are among the causes for low adoption. If properly mobilised, the use and effects of DTs could substantially be increased. Sharing of IPM DTs has a strong potential for providing wider access to the existing knowledge, for fostering IPM implementation, and for supporting plant health policies. In this article, we outline an overall approach to develop a web-based platform, in which DTs are shared and made widely available. Such a platform can include a range of ready-to-use DTs, i.e. DTs which are currently available, that have been field-validated, and which are already being used in some agricultural contexts. The platform also provides open, full documentation of DTs, makes DTs available for validation and adaptation in different agricultural contexts, and makes DTs easily available for multiple kinds of end-users involved in IPM (farmers, IPM experts, public and private information and service providers, and policy makers). We also consider how DT sharing can reduce both the technological and behavioural limitations of existing plant health management systems. [ABSTRACT FROM AUTHOR]

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle, just like traditional agriculture. The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system. Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security, reduce water scarcity, and eliminate carbon footprint. For the large-scale implementation of this farming technique, a unified system is needed that can detect crop diseases and support researchers and farmers in identifying potential causes and treatments at early stages. This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops, lettuce, basil, spinach, and parsley, grown in an aquaponics facility. First, a dataset comprising 2640 images is constructed. Then, a disease detection system is developed that works in three phases. The first phase is a crop classification system that identifies the type of crop. The second phase is a disease identification system that determines the crop's health status. The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease. The proposed approach has shown promising results with accuracy in each of the three phases, reaching 95.83%, 94.13%, and 82.13%, respectively. The final disease detection system is then integrated with an ontology model through a cloud-based application. This ontology model contains domain knowledge related to crop pathology, particularly causes and treatments of different diseases of the studied leafy green crops, which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures. The proposed application finds its significance as a decision support system that can automate aquaponics facility health monitoring and assist agricultural practitioners in decision-making processes regarding crop and disease management. [ABSTRACT FROM AUTHOR]

Digital farming has revolutionized agriculture by integrating technologies like IoT, AI, big data analytics, and remote sensing. This paper explores the impact of digital farming on agricultural engineering practices, highlighting the changes it has brought to the agri-food landscape. By using real-time data collection, analysis, and predictive modeling, agricultural engineers can make informed decisions, enabling precise and sustainable resource management. Precision agriculture technologies can reduce fertilizer and pesticide use by up to 30%, increase yields by 10–20%, and conserve water by up to 50%. Digital farming practices have also increased efficiency and productivity, with autonomous farm machinery and smart irrigation systems. Autonomous tractors operate without human intervention, freeing up farmers to focus on other tasks. Smart irrigation systems automatically adjust watering schedules based on real-time weather and soil moisture data, ensuring optimal watering for crops. The objective of this study is to demonstrate the capacity of digital farming to bring about significant changes in agricultural engineering techniques, in contrast to conventional approaches. It will examine the effects of digital farming on resource allocation, environmental sustainability, and the global food supply, thereby highlighting its potential for transformation. The research aims to inspire stakeholders in the agricultural sector to embrace digital farming as a transformative force, shaping the future of agricultural engineering practices for a more efficient, resilient, and prosperous agriculture sector. [ABSTRACT FROM AUTHOR]

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The nature of farming is – still – an essentially biological, and thus volatile, system, which poses substantial challenges to its integration into financialized capitalism. Financial investors often seek stability and predictability of returns that are hardly compatible with agriculture – but which are increasingly seen as achievable through data and digital farming technologies. This paper investigates how farmland investment brokers engage with, perceive, and produce farming data for their investors within a co-constructive process. Tackling land's 'stubborn materiality' for investment, I argue, has material and immaterial components: it includes the re-imagination of farming as a financial asset that delivers reliable income streams for investors; and the re-engineering of farmland's concrete materialities with digital farming technologies. Farmland investment brokers develop investor-suitable farmland imaginaries, underpinned by storytelling as well as the calculative 'evidence' of (digital) data. At the same time, digital technologies have become a key tool for transforming farms into 'investment grade assets' endowed with the rich data on farm performance and financial returns requested by investors. I conclude that the assetization and digitization of farmland need to be seen as closely intertwined and mutually reinforcing processes and identify key areas for future research on this intersection. [ABSTRACT FROM AUTHOR]

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated into modern farming systems, such as aquaponics, to address the challenges hindering its commercialization and large-scale implementation. Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture, that promises to address food security issues. To complement the current research efforts, a methodology is proposed to automatically measure the morphological traits of crops such as width, length and area and estimate the effective plant spacing between grow channels. Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows. The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm. For common little gem romaine lettuce, the growth is estimated within 2 cm of error for both length and width. The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system. The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions. The proposed application finds its significance as a decision support system that can pave the way for intelligent system monitoring and control. [ABSTRACT FROM AUTHOR]

The current geological epoch is characterized by anthropogenic activity that greatly impacts on natural ecosystems and their integrity. The complex networks of ecosystem services (ESs) are often ignored because the provision of natural resources, such as food and industrial crops, is mistakenly viewed as an independent process separate from ecosystems and ignoring the impacts on ecosystems. Recently, research has intensified on how to evaluate and manage ES to minimize environmental impacts, but it remains unclear how to balance anthropogenic activity and ecosystem integrity. This paper reviews the main ESs at farm level including provisioning, regulating, habitat, and cultural services. For these ESs, synergies are outlined and evaluated along with the respective practices (e.g., cover‐ and intercropping) and ES suppliers (e.g., pollinators and biocontrol agents). Further, several farm‐level ES trade‐offs are discussed along with a proposal for their evaluation. Finally, a framework for stakeholder approaches specific to farm‐level ES is put forward, along with an outlook on how existing precision agriculture technologies can be adapted for improved assessment of ES bundles. This is believed to provide a useful framework for both decision makers and stakeholders to facilitate the development of more sustainable and resilient farming systems. [ABSTRACT FROM AUTHOR]

There is a steady increase in research on livestock monitoring systems that offer new ways to remotely track the health of the livestock, early predict the diseases that may affect them and intervene in the early stages to save the situation by monitoring the various vital biodata of the livestock, as well as monitoring their feeding and tracking their location to prevent any damage or rustling. In this context, this paper comes in order to highlight and discuss the most recently published articles that study the topic of cattle health monitoring and location tracking systems using advanced IoT sensors. In addition, the research provides a review of the most important software and dashboards available in the market that can be used for this purpose. The research constitutes a reference for researchers in this field and for those who wish to develop similar monitoring systems. [ABSTRACT FROM AUTHOR]

The article substantiates the catastrophic consequences of the Russian military invasion of Ukraine, which led to the industrial crisis in the world. The need to restore the agricultural sector of the economy as soon as possible, in particular the lost or destroyed supply chains of agricultural products in the postwar period, is argued, primarily through the introduction of digital opportunities in the business processes of agricultural enterprises. The work of farmers on the ground, in fact, under the flight of missiles and fighters overhead, with long delays due to the preliminary inspection of soils by sappers, could not but affect the export of agricultural products. Although the situation with food exports before the war with Russia looked quite optimistic: in the previous year a record wheat harvest was harvested, corn harvest increased significantly, positive forecasts for food crops in the coming periods were expected. At the same time, the authors state that the introduction of quarantine measures in connection with the global pandemic has not led to a significant negative impact on the state of the agricultural sector in Ukraine. This is due to the specifics of the work of agricultural enterprises, which is carried out mainly on agricultural land, which allowed the agribusiness to reduce additional costs for compliance with the new mandatory sanitary requirements. At the same time, the global pandemic has accelerated the process of digitalization in the agricultural sector through the introduction and daily use of domestic electronic document management by agricultural enterprises, holding meetings and meetings through online platforms for video conferencing, corporate portals and others. In addition, agricultural holdings were able to quickly adapt to new working conditions through the active introduction of business processes before the spread of the global pandemic of electronic markings in agricultural production, cameras, monitors, sensors, GPS trackers, unmanned aerial vehicles. The study substantiates the view that with strong support for the development of digital agribusiness capabilities, it will be possible to further improve the process of sowing on agricultural land that was not destroyed during the war and in regions where the logistics of seed supply has not been paralyzed. As a result, there is reason to talk about the development of digital farming as a way of farming using the technologies needed to integrate financial and field records for further integrated management of the farm. However, it will significantly depend on the readiness of farmers to comprehensive digitalization of economic activity.

Abstract The current geological epoch is characterized by anthropogenic activity that greatly impacts on natural ecosystems and their integrity. The complex networks of ecosystem services (ESs) are often ignored because the provision of natural resources, such as food and industrial crops, is mistakenly viewed as an independent process separate from ecosystems and ignoring the impacts on ecosystems. Recently, research has intensified on how to evaluate and manage ES to minimize environmental impacts, but it remains unclear how to balance anthropogenic activity and ecosystem integrity. This paper reviews the main ESs at farm level including provisioning, regulating, habitat, and cultural services. For these ESs, synergies are outlined and evaluated along with the respective practices (e.g., cover‐ and intercropping) and ES suppliers (e.g., pollinators and biocontrol agents). Further, several farm‐level ES trade‐offs are discussed along with a proposal for their evaluation. Finally, a framework for stakeholder approaches specific to farm‐level ES is put forward, along with an outlook on how existing precision agriculture technologies can be adapted for improved assessment of ES bundles. This is believed to provide a useful framework for both decision makers and stakeholders to facilitate the development of more sustainable and resilient farming systems.

ABSTRACT Global consumption of oilseeds has been growing progressively in the last five growing seasons, in which soybean represents 60% of this sector. Thus, in order to maintain a high production in the region of Rio Verde, State of Goiás, against the phytopathological problems, this study aimed to define the best spectral range for the detection of H. glycines and P. brachyurus by linear regressions in soybean at R3 stage, as well as the elaboration of mathematical models through multiple linear regressions. For this, soil and root were sampled in the experimental area, as well as a flight was performed with the Sentera sensor. Data were used for the elaboration of regressions and for the validation of 2 mathematical models. Significant values were observed in simple linear regression only for cysts, in the visible range, with a good R² value for the Green, Red and 568 nm bands, to nonviable cysts. When working with the stepwise statistics, better results are found for H. glycines, which now has an R²(aj) of 0.7430 and P. brachyurus is then detected. From the mathematical model obtained with the multiple linear regression for non-viable cysts with an R²(aj) of 0.7430, it is possible to detect the spatial distribution of nematodes across the soybean field, in order to perform a localized management, optimizing the applications. Good results are also possible using the mathematical model obtained by simple linear regression.

The adoption of farm management information systems (FMIS) is on the rise at German dairy farms given their benefits in supporting and automating decision-making processes. However, the offering scope of FMIS for dairy farmers is limited, with digital services mostly focusing on animal-related data and overall economic insights. By contrast, digital energy management services (DEMS) are not yet established as an integral part of FMIS despite their expected positive contribution to a dairy farm's ecological sustainability and profitability. Against this background, the aim of this study was to find out if there is a hitherto undetected market opportunity for FMIS providers offering DEMS to German dairy farmers. To achieve this aim, the as-is market offering was screened looking at seven pre-defined DEMS, and customer preferences were investigated based on online survey responses from 74 German dairy farmers. Results of the survey indicate a high relevance of DEMS, which especially applies for optimization-oriented energy data analyses. The market coverage of such digital services, on the other hand, is not yet adequate. Hence, for providers of FMIS, we see a promising market opportunity to expand their offering by starting to deploy selected DEMS to German dairy farmers. [ABSTRACT FROM AUTHOR]

Agriculture is one of the major sectors of the global economy and also a software-intensive domain. The digital landscape of agriculture is composed of multiple digital ecosystems, which together constitute an agricultural domain ecosystem, also referred to as the "Agricultural Data Space" (ADS). As the domain is so huge, there are several sub-domains and specialized solutions, and each of them poses challenges to interoperability. Additionally, farmers have increasing concerns about data sovereignty. In the context of the research project COGNAC, we elicited architecture drivers for interoperability and data sovereignty in agriculture and designed a reference architecture of a platform that aims to address these qualities in the ADS. In this paper, we present the solution concepts and design decisions that characterize the reference architecture. Early prototypes have been developed and made available to support the validation of the concept. [ABSTRACT FROM AUTHOR]

Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops. [ABSTRACT FROM AUTHOR]

Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate environmental conditions (temperature, illumination, humidity, etc.), improper nutrient supply, and poor water quality. Various techniques have been developed over the years to identify leaf chlorosis and assess the quality of crops, including visual inspection, chemical analyses, and hyperspectral imaging. However, these techniques are expensive, time-consuming, or require special skills and precise equipment. Recently, computer vision techniques have been implemented in the agriculture field to determine the quality of crops. Computer vision models are accurate, fast, and non-destructive, but they require a lot of data to achieve high performance. In this study, an image processing-based solution is proposed to solve these problems and provide an easier, cheaper, and faster approach for identifying the chlorosis in lettuce crops grown in an aquaponics facility based on their sensory property, foliage color. The 'HSV space segmentation' technique is used to segment the lettuce crop images and extract red (R), green (G), and blue (B) channel values. The mean values of the RGB channels are computed, and a color distance model is used to determine the distance between the computed values and threshold values. A binary indicator is defined, which serves as the crop quality indicator associated with foliage color. The model's performance is evaluated, achieving an accuracy of 95%. The final model is integrated with the ontology model through a cloud-based application that contains knowledge related to abiotic stresses and causes responsible for lettuce foliage chlorosis. This knowledge can be automatically extracted and used to take precautionary measures in a timely manner. The proposed application finds its significance as a decision support system that can automate crop quality monitoring in an aquaponics farm and assist agricultural practitioners in decision-making processes regarding crop stress management. [ABSTRACT FROM AUTHOR]

Digital cultivation is emerging as one of the most promising fields that helped in creating an ecosystem for smart farming. Precision farming, modernized techniques, and creating smart agriculture supply chains are the need of the hour for high-quality yield. Artificial Intelligence (AI) helps create a framework and plays an important role in making decisions by analysing various data points. There are countries where more than 70% of the population depends on agriculture for their living, technological advancements help to improve crop yields and get better farming results through sustainable ways. Each stage in agriculture, starting from preparation of land, crop selection, type of fertilizer to use, to the kind of watering needed for the crops; can be monitored and regulated by technological advancement. Farmers can also make decisions and implement the best practices in their field by using AI and allied technologies. Disruptive technologies such as blockchain, the Internet of Things, remote sensing, imaging technologies, and drones can transform the primitive way of agriculture. Market analysis and user demands can also be foreseen, which helps farmers to get better yields. Another important sector where technology can play a big role in disease control and pest management. Artificial Intelligence-based farming creates high productivity and better yield, increasing individual farmers' profit. In this study, the authors would like to throw light on AI and allied technologies, which can make agricultural productivity increase significantly. In a post-pandemic situation, high-yield and more productive farming will have a major impact. An agricultural intelligence framework model for self-sustained farming is proposed in this work. The proposed framework will help achieve self-sustained growth with increased economic stability. An end-to-end supply chain ensures customers are provided with quality products and farmers are not financially looted. Technology-driven farming will also push the next generation to take up agricultural jobs. The various advancements and strategies we propose in this study aim to build a better ecosystem for transforming Artificial Intelligence into agricultural intelligence. • Agricultural Intelligence Models for Enhanced Yields: Insights into leveraging AI for sustainable farming practices. AI's pivotal role in decision-making for more innovative farming. • Technological Advances Boosting Crop Yields: Benefits of integrating technology for agricultural improvements. Examples demonstrating the impact of tech advancements on farming. • Precision Farming Driving Sustainability: Exploring precision farming's role in sustainable agriculture. Modern techniques are fostering sustainable supply chains. • Backed by Research and References: Citing supportive research studies and articles. Contributing to the knowledge base on technology in agriculture. [ABSTRACT FROM AUTHOR]