Precision Agriculture System Research Articles

Modeling and investigation of the hydrological situation

Thousands of earthen dams have been built in Russia to create accumulating reservoirs, protect low-lying areas from floods, hydropower and others. Sometimes, due to the presence of underground leaks, erosion of the dam occurs and a breakthrough with catastrophic consequences. Therefore, the task of timely identification of places of underground leaks of earthen dams is urgent. Objective – to show the results of a flight experiment and the practical use of a microwave radiometer mounted on an unmanned aerial vehicle (UAV) in order to monitor the condition of an earthen dam in the Voronezh region and the possibility of searching for places of underground leaks. The paper presents the results of an experimental study of the condition of earthen dams. Also, the analysis of the obtained soil moisture maps allows you to determine the locations of possible underground leaks. The results of a flight experiment using a bipolarization microwave radiometer placed on board an unmanned aerial vehicle for remote determination of a portrait of soil moisture can be used to search for places of possible underground leaks. Also, this method of remote measurement of soil moisture can be used for the needs of agriculture, in particular for precision farming systems. In addition, the paper assesses the prospects of using microwave radiometric moisture meters for operational examination of the state of extended hydrological objects.

DEVELOPMENT OF THE SOWING SYSTEM OF THE SOWING COMPLEX FORINTERNAL-SOIL DIFFERENTIATED MINERAL FERTILIZER WITH SIMULTANEOUS SOWING OF CEREAL

The issue of increasing the effectiveness of post-sowing intra-soil differentiated application of the main and starting dose of granular mineral fertilizers in the system of precision agriculture is considered. The need to use differential application of granular mineral fertilizer and machines that ensure high-quality performance of this technological process, taking into account the heterogeneity of the content of nutrients in the soil in certain areas of the field, is indicated. It is also indicated the need to develop and introduce into production highly accurate automated information technologies based on the use of high-performance means of mechanization. The indicated shortcomings of modern seeding machines: lack of adaptability for simultaneous differentiated introduction of the main and starting dose, as a result of which they lack highly adapted dispensers, appropriate control and management systems. The construction and technological scheme of the seeding complex is proposed and the pneumatic seeding system of this unit is developed for post-sowing intra-soil differentiated application of the main and starting dose of granular mineral fertilizers in the precision farming system. The proposed seeding complex will make it possible to eliminate the excessive application of mineral fertilizers, which can cause an increase in the activation of microbiological processes in the soil and have negative ecological consequences, leading to the deterioration of the physic-chemical and biological properties of the soil. The use of resource-saving technologies of precision agriculture in the agro-industrial complex of Ukraine will allow to ensure the management of production processes at the expense of information technologies, robotic and automated systems, which will allow to bring domestic agricultural enterprises to a new level.

СУЧАСНІ РІШЕННЯ ТА НАПРЯМКИ РОЗВИТКУ ОСНОВНИХ ЕЛЕМЕНТІВ СИСТЕМИ ТОЧНОГО ЗЕМЛЕРОБСТВА

One of the most advanced directions in agriculture is precision agriculture, which is an integrated process of managing plant growth according to their needs. This technology has become possible due to the development of informatics, communication systems, and progress in the automation of agricultural machinery. A fundamental part of precision agriculture is the development and adaptation of strategies and practices for farming in modern conditions. The main focus is on measuring, understanding, and applying practical factors that affect plants, such as soil hydro-physical and chemical properties, landscape, seeds, applied technology, sowing and harvesting schedules, diseases and pests, weeds, and agro-climatic conditions. The purpose of the work is to explore innovative, modern solutions, and development directions of the basic elements of precision agriculture systems that will contribute to making informed management decisions to ensure food security in agricultural production. The work emphasizes that precision agriculture is considered an integral part of resource-saving ecological farming, involving the application of an integrated management system rather than individual separate elements. Thanks to the rapid development of information technologies based on innovative achievements in cybernetics, optics, sensor systems, laser and computer technology, satellite navigation systems, sensors of various purposes, and wireless communication means, solutions have been developed that integrate all elements of the agricultural machinery complex into a single whole. Among the modern solutions and development directions of precision agriculture systems are the application of satellite remote sensing with spatial resolution up to a few centimeters (GeoEye and WorldView); UAVs equipped with multispectral cameras, various sensors, satellite navigation systems, a small onboard computer, and equipment for chemical application; ground sensors designed for continuous (mobile) monitoring of plant and soil properties, such as nitrogen content, water stress, organic matter, and soil moisture (ISARIA, MiniVeg-N, and YARA N); and the use of systems such as Agronomic Decision-making Engine (ADE), See & Spray Ultimate, AiCPlus, SmartSprayer, etc. It is noted that with the development of electronics and information technologies, various sensor systems have emerged, widely used in agricultural production to control the production process. To successfully implement precision agriculture technologies, reliable information about the soil, plants, the surrounding environment, and their variability in both space (within a field) and time is necessary.

Factors influencing fruit cracking: an environmental and agronomic perspective.

Fruit cracking, a widespread physiological disorder affecting various fruit crops and vegetables, has profound implications for fruit quality and marketability. This mini review delves into the multifaceted factors contributing to fruit cracking and emphasizes the pivotal roles of environmental and agronomic factors in its occurrence. Environmental variables such as temperature, relative humidity, and light exposure are explored as determinants factors influencing fruit cracking susceptibility. Furthermore, the significance of mineral nutrition and plant growth regulators in mitigating fruit cracking risk is elucidated, being calcium deficiency identified as a prominent variable in various fruit species. In recent years, precision farming and monitoring systems have emerged as valuable tools for managing environmental factors and optimizing fruit production. By meticulously tracking parameters such as temperature, humidity, soil moisture, and fruit skin temperature, growers can make informed decisions to prevent or alleviate fruit cracking. In conclusion, effective prevention of fruit cracking necessitates a comprehensive approach that encompasses both environmental and agronomic factors.

Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI

The prediction of cereals yields today is very important for global food security and helps decision-makers in the import-export operations of countries, especially with the rise world population. The advent of remote sensing technologies in precision farming systems has made cereal yield predictions possible, providing valuable insights into the temporal and spatial variations in cereal conditions across both large and small-scale crop lands. Among the various vegetation indices used to analyze these conditions, the normalized difference of vegetation index (NDVI) has emerged as a key indicator. The main objective of this study is to evaluate the possibility of using MODIS-NDVI data to forecast the yield of cereal crops (wheat and barley) in semi-arid region of Algeria (Setif). Additionally, identify the optimal timing for reliable and accurate crop yield forecasts. The remote sensing data utilized in this study covered the growing seasons from February to June, from 2002 to 2022. The results indicated a strong correlation between cereal grain yield and NDVI from late February to mid-March, with R² values ranging from 0.55 to 0.82 for the two cereal species. The RMSE of the NDVI based prediction model ranged from 0.01 t ha-1 to 0.276 t ha-1. The approximate average increase in the grain yield of barley and wheat lies between 0.659 to 0.746 t ha-1 with an increase of 0.1 in NDVI value. These results demonstrate the effectiveness of using MODIS-NDVI data for cereal yield forecasting in semi-arid region of Algeria, offering valuable predictions two to three months before the harvest.

Preface

The International Conference on Agriculture, Environment and Food Security (AEFS) is a prominent global conference organised by the Faculty of Agriculture at Universitas Sumatera Utara, Indonesia. It provides a platform for sharing innovative and practical insights in agriculture, environment, and food security. Furthermore, it facilitates global communication among scientists, researchers, academics, resource managers, practitioners, students, policymakers, and everyone interested in agriculture, environmental sciences, and food security.The AEFS Conference has been conducted every year since 2017. The AEFS Conference has focused on the following themes over the past four years: Agriculture, Plantation, and Livestock for World Food Security (AEFS 2017); Prospects and Challenges of Sustainable Agriculture to Improve Food Security and Environment (AEFS 2018); Challenges on Food Security and Sovereignty in the Industrial Era 4.0 (AEFS 2019); Innovation and Technologies on Agricultural and Environmental Sustainability Systems (AEFS 2020); Precision Agriculture Technologies and Farming Systems Towards Food Security and Sustainability (AEFS 2021); Strengthening Sustainable Agriculture for A Better Future (AEFS 2022). In addition, the 7th AEFS 2023 had a theme centred around “Green Economy for Enhancing Food Security, Environmental and Agricultural Sustainability.” The event featured seven specific tracks, namely Agricultural Engineering, Agricultural Economics, Food Science and Technology, Animal Science, Marine and Fisheries Science, Environmental Science, and Plant Science (including Agronomy and Plantation, Plant Breeding, Biotechnology, Integrated Pest Management, and Soil Science).List of Steering Committee and Organizing Committee are available in this Pdf.

Characterizing Diploid and Tetraploid Potato Cultivars with Reflectance Spectroscopy

Characterizing potato cultivars is essential for diagnosing the specific production and management requirements. Employing techniques that facilitate rapid, objective, and cost-effective identification of various aspects of plant physiology is crucial for generating this valuable information. In this study, our goal was to identify different potato cultivars across three distinct phenological phases utilizing reflectance spectroscopy within the visible and near-infrared ranges. Reflectance spectra were measured using a portable spectrometer on leaves obtained from five different potato cultivars. We constructed classification models to determine the accurate classification percentages for each cultivar within specific phenological stages. General trends in the reflectance spectra were observed, which were consistent across all five cultivars within the measured range. During the vegetative phase, there was a uniform reflectance of around 5% in the green region. This value experienced a slight decrease during the flowering phase and subsequently rose to approximately 12% during the senescence phase. In the near-infrared (NIR) range, a reduction in reflectance from 50 to 30% was noted during the flowering phase, followed by a cultivar-dependent increase towards senescence. The specific wavelengths identified in the spectra enabled the accurate classification of plants from each cultivar across the vegetative, flowering, and senescence phases. The classification rates were notably high, with a success rate of 87% for Var. Paola, 96% for Var. Paysandú, 93% for Var. Violeta, and 100% for cv. 448 and 440. The Var. Paola exhibited superior correct classification rates, ranging from 85% for Paysandú and Violeta, 90% for cv. 440, to 100% for cv. 448. The characterization of potato cultivars using spectroscopy techniques yields valuable information that serves as fundamental input for precision agriculture systems.

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species.

Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease and species specific and contingent upon a multitude of factors. The fields of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus (WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish, and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease-resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favorable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate-to-high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems and AI algorithms and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development.

Deep Learning based Mango Leaf Disease Detection for Classifying and Evaluating Mango Leaf Diseases

Mango is one of the important commercial crop in the world. It provides nutritional and financial support to human life. Different diseases of leaves impact the health of the mango crops. The early and proper pest control measurement can prevent large output losses. We propose an automated inspection and classification of disease-affected mango leaves that uses Deep Learning (DL) model. Our DL model-empowered Convolutional Neural Network (CNN) architecture is trained with an extensive image dataset of mango leaves portraying a variety of disease indications at both low and high-resolution images. The objective is to be able to identify accurately the disease type on mango leaves including Bacterial Canker, Powdery mildew, Anthracnose, Gall midge, and Sooty mould. Crops can develop gradual immunity with reasonable pest control and can purposively shaped them against constantly evolving environment. The proposed system will be effective and it will definitely prove a facile system to be used as a key component of a novel precision agriculture system as will be presented in our future work. The performance of the proposed system is augmented through the utilization of transfer learning techniques and pre-trained models, including VGG-16, MobileNet, Googlenet, YoloV8, and EfficientNet. These Deep Learning models not only offer an accurate and efficient approach for classifying diseases in mango leaves but also provide valuable insights into the severity of the identified diseases. Utilizing this information to support farmers and agricultural professionals in making informed decisions pertaining to disease management and treatment strategies can significantly contribute to the sustainable growth of mango crops. The development and implementation of such automated technologies have the potential to revolutionize the monitoring of mango crop health, enabling early disease detection and enhancing crop yields.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.

КОНЦЕПТУАЛЬНІ ЗАСАДИ ФОРМУВАННЯ СТРАТЕГІЧНИХ НАПРЯМКІВ РОЗВИТКУ АГРАРНИХ ПІДПРИЄМСТВ В УМОВАХ ЦИФРОВІЗАЦІЇ ЕКОНОМІКИ

The article examines the conceptual foundations of the formation of strategic directions for the development of agrarian enterprises in the conditions of digitalization of the economy. It is indicated that digitalization is used by agricultural enterprises to change the business model, which provides the formation of new revenues and opportunities for the creation of a new type of value chains. It is emphasized that a characteristic feature of digitalization is that it is the foundation of the Fourth Industrial Revolution and is one of the main currents of the third wave of globalization processes. It has been proven that digital technologies provide many opportunities and perspectives for the creation of smart agriculture, giving their consumers a great impetus to transform their activities. It is indicated that in agriculture, digital technologies can modernize the industry, facilitating the implementation of innovations in agribusiness and creating new opportunities for agricultural enterprises in areas such as the biological sector, ecosystem sustainability, etc. It is noted that among the innovative technologies used by agribusiness entities are: the introduction of precision farming systems, aerial photography for the purpose of monitoring the quality of crops, keeping the history of fields for choosing the optimal crop, laboratory studies of the soil to obtain information about the biochemical composition. Digital technologies, classified by the degree of influence on the development of agricultural enterprises, are highlighted. The basic and auxiliary conditions of digitalization of agricultural production have been studied. Groups of risk analysis tasks during the formation of strategic intentions of an agrarian enterprise are given. The key aspects of the sustainable development of an agricultural enterprise are proposed. It was concluded that the digitalization of the agricultural sector is one of the main components of the digital policy of the state and a determining factor in the growth of the economy in general, in particular, the digital industry itself as a technology producer. The introduction of digital technologies is a positive trend not only for agricultural enterprises of Ukraine, because at this stage of digitalization, it is extremely important to apply a whole set of measures from the state for the development of digitalization in our country.

Analysis of directions for application of artificial intelligence systems in soil condition analysis

The most significant characteristics of the soil are water-physical, biological and other properties, by influencing which it is possible to increase the productivity of agricultural crops. Analyzing various soil characteristics is a very labor-intensive and expensive process. The authors reviewed research on the use of artificial intelligence systems in the field of soil analysis. The purpose of the study is to analyze research on the use of artificial intelligence systems in the field of soil analysis. The article presents an analysis of artificial intelligence systems based on artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), multiple regression (MR) methods, particle swarm algorithm (PSO) and also their ensembles (various combinations). The use of these systems (their models) makes it possible to predict various types of soil erosion, predict soil temperature and moisture depending on various conditions, and also predict such complex indicators as the cation exchange capacity of the soil. The scope of application of artificial intelligence systems is to increase the awareness of managers and specialists of agricultural organizations and industry management bodies about various soil characteristics. This, in turn, allows you to make optimal decisions on various agricultural activities. Artificial intelligence systems can process significant amounts of data with high accuracy and speed compared to humans, which makes it possible to more effectively analyze many factors affecting the condition of the soil. The use of artificial intelligence systems can significantly reduce the cost of analyzing various soil characteristics and is a necessary condition for the use of precision farming systems.

Development of a precision farming system based on the use of UAVs for spraying pesticides and fertilizers

Further effective development of the precision farming system is largely associated with the use of UAVs for spraying pesticides and fertilizers. The article analyzes the characteristics of UAVs for spraying pesticides and fertilizers (spraying drones) and notes the advantages of UAVs for aerial spraying, as well as the problems that hinder their use. Previously published work discusses the use of agricultural UAVs to spray liquid pesticides, but it should be noted that dry crop protection products, including biological pesticides and dry fertilizers, can also be used. Conditions are given that require the expanded use of UAVs for spraying pesticides and fertilizers as part of the further development of a precision farming system. These conditions confirm that the development of a UAV-based precision agriculture system for spraying fertilizers and pesticides for plant protection is currently a viable option when choosing unmanned aerial equipment. The swarm (or group) use of agricultural UAVs is promising. In this case, research into the microprocessor performance of drone swarms is of particular importance to improve the safety of UAV transport and technological cycles, which combine ground and airborne operations performed by both the operator and the components of the unmanned aircraft system.

Potential Changes in Land Use and Plant Production in Poland in the Context of Implementing the European Green Deal

The purpose of this paper is to identify the potential changes in agricultural land use in Poland as a consequence of implementing the European Green Deal, including its potential economic effects. The study covered crops of key importance to Polish agriculture, cultivated in a total area of 8.2 million ha, which accounts for more than 60% of land in good agricultural condition. The analyses were based on average values for 2016–2020 and used three scenarios for EGD implementation by 2030 (no EGD; partial implementation of the EGD; full implementation of the EGD). Each analysis took account of changes in the area of land under conventional, organic and precision farming systems. The study found that EGD requirements can be met by implementing the precision farming system, especially for intensive farming schemes like rape and sugar beet. Organic farming should be used either for extensively farmed cereals like oat or for fruits which are sold at higher prices when farmed organically. Another finding is that the full implementation of the EGD could decrease the production volume of key Polish crops by 13%, which can have important social impacts, such as increased prices of food products or a deterioration of food security.

Управлінські стратегії розвитку бізнесу в умовах цифрової трансформації

Digital transformation, based on the latest technologies, is a strategic course for enterprises aimed at optimizing, expanding their customer base, and opening new sources of profit. The article examines cloud computing, artificial intelligence, the Internet of Things, blockchain, and data analytics as critical aspects of digital transformation, focusing on their impact on business. A study of the existing opportunities for attracting digital technologies to the agribusiness sector was carried out, with an understanding of the need to use the latter not only in the management sphere but also in the production sphere, based on the specifics of the industry. The article presents the results of the digital activity of individual companies that use digital technologies to increase efficiency and productivity. It is determined that examples of task automation, the use of GPS navigation systems for tractors, and field monitoring via unmanned aerial vehicles are just a few illustrative examples of digital transformation. Artificial intelligence is being used to analyze crop data, improve understanding of operations, and identify opportunities for improvement. Practical examples include introducing precision farming systems, which help increase productivity and resource efficiency. Logistics and warehouse management systems help optimize machinery use and inventory management. Digital transformation in agribusiness not only increases the competitiveness of enterprises but also provides new opportunities for operating on digital platforms. The article explores the importance of researching digital opportunities in the manufacturing sector to support the digitalization of agribusiness and increase its competitiveness. The article emphasizes the importance of digital transformation in the manufacturing sector, which can determine the success of the Ukrainian business industry in the context of rapid digital development. Keywords: digital transformation, digital adaptation, digital technologies, strategic management, data analytics, innovation, competitive development.

Model for Detecting Boom Height Based on an Ultrasonic Sensor for the Whole Growth Cycle of Wheat

Ultrasonic feedback energy is affected by the variety, planting, and growth state of crops; therefore, it is difficult to find applications for this energy in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, the jointing stage, the booting stage, the heading stage, the filling stage, and the maturity stage. The relationship between the actual value of the height from the spray boom at the top of the wheat (Habw) and the detected value of the height from the spray boom at the top of the wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤50 mm during the full growth cycle. This study provides a method for constructing a boom height detection model based on the whole growth cycle of wheat, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages. The proposed method solves the problem of low accuracy of repeated detection of low-cost ultrasonic sensors in different environments and can provide technical support for improving field applications of the boom height control system based on ultrasonic sensors.

An Overview of Precision Farming

With respect to conventional farming precision agriculture increases average yields by limiting the wastage by calculating the exact required quantities of inputs. One major issue in India is the relatively small and scattered landholdings. In India 58% of the cultivable land is less than 1ha under single owner. The agricultural production system is the result of a complex interplay between seed, soil, water, and agrochemicals (including fertilizers). As a result, judicious control of all inputs is critical for the long-term viability of such a complex system. Precision agriculture is the use of technology and techniques to control the geographical and temporal variability associated with all aspects of agricultural production to improve output and environmental quality. Precision agricultural success is dependent on an accurate assessment of variability, its management, and evaluation in the space-time continuum of crop production. Precision agriculture's agronomic performance has been highly impressive in sugar beet, sugarcane, tea, and coffee crops. Due to lack of knowledge of space-time continuum the economic benefits environmental and social advantages are not explored yet. Precision agriculture is a relatively new field that integrates cutting-edge geographic technology with farming scenarios to optimize inputs, eliminate waste, and maximize returns. Precision farming systems are intended for use in many sorts of agricultural systems, ranging from row crops to dairy, and the technology has experienced extensive acceptance in the United States and across the globe.

Methodology of operational monitoring of crop status based on the internet of things technologies

Digital technologies are being actively introduced into Russian agriculture at different levels of information analysis (from the plot to the field, farm, region and country as a whole). In crop production at the field level, one of the most important values is the introduction of systems for accurate, rapid and automated monitoring of crop condition, the success of which largely predetermines the effectiveness of precision farming systems. The aim of the research is to develop a methodology for using Internet of Things technologies for non-contact monitoring of crops and related meteorological and soil-hydrological parameters. A wireless network is used as the basis for monitoring, which includes sensor nodes equipped with sensors for meteorological parameters, soil moisture and cameras equipped with a fish-eye lens. Sensor nodes equipped with sensors and cameras are placed in the field according to a specially designed scheme, individualized for each field. Development of the scheme of sensor placement on the field is based on the analysis of long-term archives of satellite data of high spatial resolution and refined soil maps of large scale. Information from sensors is wirelessly transmitted to the network coordinator (or base station) and then to the remote server in the database, and there it is automatically analyzed and interpolated for the whole field. Based on the analysis, recommendations for correction of agrotechnology of crop cultivation are formed. Elements of the methodology were tested on a number of test fields and showed high efficiency. Implementation of the proposed approaches can serve as an alternative to the use of remote sensing data for crop monitoring in offline precision farming systems.

Productivity analysis of agricultural UAVs by field crop spraying

The article presents an analysis of the performance of agricultural UAVs when spraying field crops. Based on an analysis of the current state of technology for using UAVs in precision agriculture, a model for calculating UAV performance has been developed, based on the time structure of the crop spraying process, including the time of spraying, replenishment of mineral fertilizers or pesticides, and UAV flight. It is noted that for an agricultural UAV, when used in a precision farming system, the equation of existence must relate the take-off mass, the functional components of the take-off mass and the parameters of the sprayer, ensuring differentiated application of pesticides and fertilizers. Based on the proposed performance model of agricultural UAVs, recommendations are given for choosing the type of UAV for spraying crops. This model allows us to generalize methods for testing the production performance of agricultural UAVs, build models of production efficiency and provide proposals for technological solutions to improve these performance characteristics.

Investigating Strategies and Practices to Enhance the Resilience of Agricultural Supply Chains in the Face of Disruptions and Uncertainties

Amidst disruptions and uncertainties, this research examines strategies to increase the resilience of agricultural supply chains. Factors such as diversification, adoption of technology, and climate-resilient agricultural practices emerge to fortify the robustness of supply chains. These events look set to become more common, and survey results point emphatically in the direction of diversifying sources and markets. Case studies offer a model for successful strategies against an uncertain future. Recognized as a resilience enabler, technology is challenged by limited access and concerns about data security and interoperability. Precision farming and geographic information system mapping are among the climate-resilient practices that help stabilize crop production. Financial limitations, which disproportionately affect small and medium-sized enterprises, prevent the implementation of resilience measures. Differences in regulatory standards across the regions create bottlenecks, and all policies have to be standardized while still being locally sensitive. The research argues for strategic interventions, such as financial support mechanisms and skills development programs to remove obstacles limiting resilience. These results have far-reaching potential to inform policymakers, industry participants, and researchers about how to build an integrated regulatory environment for innovation in technology.. KEYWORDS :Agricultural supply chains, Climate-resilient practices, Diversification, Financial constraints, Resilience, Technology adoption