Precision Agriculture Technologies Research Articles

Climate Crisis and Agricultural Response: Climate Resilient Crops for Sustainability in Food Production Systems

Climate change encompasses altered levels of temperature and humidity, variability in the rainfall pattern, fluctuations in weather parameters, rise in ambient CO2 levels, emission of greenhouse gases, global warming, etc. that results in extreme events and disasters as cyclones, floods, droughts, salinity, nutrient and heavy metal stress, change in arthropod diversity and emergence of new invasive pests. This results in un-usual effects in agro-ecosystems leading to changes in cropping patterns, crop diversity, and their interaction with biotic and abiotic stress factors, threatening livelihood, food, and nutritional security. Population displacement, declining food productivity, and vulnerable agro-ecosystems are the major consequences of altered meteorological events that occur due to climate change. Therefore, substitution of traditional crops with crops that exhibit resilience to climate crisis is the need of the hour. Smart breeding approaches and precision farming technologies as remote sensing and spectral analysis, artificial intelligence, machine learning, speed breeding, genome analysis, genetic manipulation, gene drive systems, system biology study, omics approaches, etc can make agricultural production climate resilient and sustainable. Response of biofortified crops under changing climate must also be assessed to improve the crop productivity and output.

Whole-farm yield map datasets – Data validation for exploring spatiotemporal yield and economic stability

CONTEXTStatistical methods used for delineation of field management zones and yield stability are frequently only applied to relatively small areas, with few studies performing rotational, whole-farm economic spatiotemporal appraisals. To enable accurate economic analysis, yield map datasets must contain minimal errors while cleaning procedures are often used to remove errors, it is rare that cleaned data is validated before its application. OBJECTIVEThe objective of this study was to process, validate and combine spatial statistical approaches for a rotational yield map dataset from a whole-farm across 7 crops in a winter wheat based rotation. Developing a framework for using validated yield map datasets to support precision agriculture techniques that are applicable for farm-level decision making. METHODSThe rotational completeness of a 10 year combine yield map dataset for a 435 ha farm in Eastern England was assessed. The dataset was cleaned statistically, and its accuracy assessed by comparison with recorded yields from trailer weigh cells. The cleaned, validated, and corrected yield map dataset was used to identify management zones across the whole farm using fuzzy clustering. The temporal stability of management zones and economic performance across the rotation was also assessed. RESULTS AND DISSCUSIONData cleaning methods removed 16% of data points, improving the degree of spatial correlation within the individual yield maps. Independent validation demonstrated varied accuracy of yield maps from combine harvester data and errors in wheat ranged from 0.53 to 1.53 t/ha RMSE. These errors have implications for researchers using combine yield data to develop and validate precision agriculture technologies. This data set required correction before yield data can be applied with confidence for on-farm decision making. Compared to the zones with the highest margin in each field, 34% of zones had an average annual margin loss of >£100 ha. The temporal stability of the resulting management zones also varied. Areas with the lowest economic performance and greatest yield stability across years will potentially see the greatest economic and environmental benefits from precision agriculture techniques. SIGNIFICANCEThe accuracy of combine yield map data should not be assumed. The application of these datasets, including for the identification of management zones or in developing precision agriculture techniques should attempt to address this through data cleaning and validation procedures. Only then should it be used for on farm decision making, such as identifying areas with the most economic benefit by applying precision agriculture tools such as variable rate nutrient applications.

Early adopter insights on physical impact mill technology for harvest weed seed control in Canada

The evolution and spread of herbicide resistance among the weed community has increased interest in alternative weed management strategies such as harvest weed seed control. Western Canadian producers have begun adopting physical impact mills as an additional weed management strategy. A survey of early adopters of physical impact mill technology in Canada was conducted to better understand the motivations behind producers adopting, initial experiences, and research needs. Ten producers responded to the survey, accounting for 18 out of an estimated 30 impact mills in use in Canada, believed to be located primarily in the Canadian Prairies. These producers were mainly from larger farms (> 4,000 ha), equipped the majority of their combines (75% average) and used the mills in essentially all crops grown. The majority of respondents were located in Saskatchewan, with 2 mills being used in Alberta. Wild oat (60%) and kochia (50%) were the weeds most frequently mentioned as specific motivators of impact mill adoption. Average increased fuel cost from the mill was estimated at CAD $3.46 ha-1, with average annual maintenance costs of about $1,500 per impact mill. Producers relied on information from mill companies and other early-adopting farmers primarily, followed by extension talks and social media. Research needs were also identified by producers that could inform the future direction of harvest weed seed control research in Canada. Future research should focus on confirming efficacy, optimizing combine settings, and looking at integrated systems with precision agriculture technologies.

Willingness to adopt precision agriculture: An analysis of Gombe and Bauchi States of Nigeria

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

Deep Learning Algorithms for IoT Based Crop Yield Optimization

Precision agriculture, with its objectives of optimizing crop yields, decreasing resource waste, and enhancing overall farm management, has emerged as a revolutionary technology in modern agricultural practices. The advent of deep learning techniques and the Internet of Things (IoT) has brought about a paradigm shift in monitoring, decision-making, and predictive analysis within the agriculture industry. This review paper investigates the relationship between deep learning, the (IoT), and agriculture, with an emphasis on how these three domains might work together to optimize crop yields through intelligent decision-making. The integration of deep learning techniques with (IoT) technology for precision agriculture is thoroughly analyzed in this study, covering recent developments, obstacles, and possible solutions. The paper investigates the role of deep learning algorithms in analyzing the vast amounts of data generated by IoT devices in agriculture. It scrutinizes various deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants applied for crop disease detection, yield prediction, weed identification, and other crucial tasks. Furthermore, this review critically examines the integration of IoT-generated data with deep learning models, highlighting the synergistic benefits in enhancing agricultural decision-making, resource allocation, and predictive analytics. This review underscores the pivotal role of IoT and deep learning techniques in revolutionizing precision agriculture. It emphasizes the need for interdisciplinary collaboration among agronomists, data scientists, and engineers to harness the full potential of these technologies for sustainable and efficient farming practices.

Digital transformation metamodel in smart farming: Crop classification prediction based on recurrent neural network

Agriculture 4.0 is an opportunity for farmers to meet the current challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies. Agriculture 4.0 enables farms to create added value by combining innovative technologies, such as precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is highly sensitive to strategic changes in organizational structures, objectives, modified variety, new business objects, processes, etc. To control the farm’s information system strategically, we proposed a metamodel based on the ISO/IS 19440 standard, where we added some new constructs relating to advanced digital technologies for smart and connected agriculture. We applied the proposed metamodel to the crop classification prediction process. This involved using machine learning methods such as recurrent neural networks to predict the type of crop being grown in a given agricultural area. Our research bridges farming with modern technology through our metamodel for a connected farm, promoting sustainability and efficiency. Furthermore, our crop classification study demonstrates the power of advanced machine learning, guided by our metamodel, in accurately predicting crop conditions, emphasizing its potential for crop management and food security. In essence, our work advances the transformative role of digital agriculture in modern farming.

Статистически групировки при изследване нагласите на фермерите за внедряване практиките на прецизното земеделие

The application of precision farming techniques leads to significant improvements both in economical and environmental point of view, facilitates the optimization of resource use and reduces farmers’ risks in terms of natural and disease hazards but enhances the risk from technological faults. The whole article is to explore the attitudes of farmers regarding the possibilities and prospects for the implementation of precision agriculture technologies, identifying and establishing the most possible and unifying views and perceptions obtained from a field study. In this article, a statistical grouping is used to process surveys conducted in farms in Bulgaria. Given the growing role of technology and its importance in expanding, improving and stabilizing farms, the role of precision agriculture is becoming stronger in the agriculture industry.

Development Of Agriculture Spraying Drone

The primary objective of the AGRICULTURE SPRAYING DRONE is to enhance the efficiency of agricultural spraying through the utilisation of unmanned aerial vehicles (UAVs) and also to minimise waste and adverse effects on the environment while optimising conventional agricultural spraying techniques. With the help of a 22000 mAh battery, the system combines cutting-edge parts, including the Hobby wing X6plus motors, K++ V2 flight controller, Sky Droid T12 receiver and transmitter, Hobby wing pump, flowmeter, Can hub, obstacle avoidance system, and PMU. The drone, with a total weight of 29 kg and a maximum payload capacity of 12 kg, guarantees accurate and productive spraying operations across large agricultural areas. Key characteristics include adaptive spraying patterns tailored to specific field conditions, targeted pesticide application, and real-time crop health monitoring. An additional mobile application enhances the system by offering farmers the ability to remotely operate, schedule spraying, and examine field data. In order to show how the suggested drone system may be implemented practically and its effectiveness assessed in actual agricultural settings, the article describes the system architecture and provides a use-case scenario. By enabling more effective and sustainable agricultural methods, this research advances precision agriculture technologies. Key Words: Agriculture Spraying Drone, Precision Spraying, Sustainability.

Democratization through precision technologies? Unveiling power, participation, and property rights in the agricultural bioeconomy

This piece addresses the political dimension of sustainability in the agricultural bioeconomy by focusing on power, participation, and property rights around key technologies. Bioeconomy policies aim to establish economic systems based on renewable resources such as plants and microorganisms to reduce dependence on fossil resources. To achieve this, they rely on economic growth and increased biomass production through high-tech innovations. This direction has sparked important critique of the environmental and social sustainability of such projects. However, little attention has been paid in the bioeconomy literature to the political dimension surrounding key precision technologies such as data-driven precision agriculture (PA) or precision breeding technologies using new genomic techniques (NGT). The political dimension includes questions of power, participation, and property rights regarding these technologies and the distribution of the benefits and burdens they generate. This lack of attention is particularly pertinent given the recurring and promising claims that precision technologies not only enhance environmental sustainability, but also contribute to the democratization of food and biomass production. This contribution addresses this claim in asking whether we can really speak of a democratization of the agricultural bioeconomy through these precision technologies. Drawing on (own) empirical research and historical evidence, it concludes that current patterns are neither driving nor indicative of a democratization. On the contrary, corporate control, unequal access, distribution, and property rights over data and patents point to few gains for small firms and breeders, but to a reproduction of farmers' dependencies, and less transparency for consumers.

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.

Evaluating the effectiveness of precision farming technologies in the activities of agricultural enterprises

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

DETERMINING FACTORS IN THE ADOPTION OF PRECISION AGRICULTURE TECHNOLOGIES BY GRAIN PRODUCERS

The adoption of precision agriculture technologies (PAT) has contributed significantly to the development of a more sustainable agriculture with a greater use of inputs, cost reduction, and increased productivity. Also, information arising from reports generated using these technologies allows farmers to manage their property more efficiently. Despite the benefits to the management of production and use of inputs, many farmers still do not use precision agriculture technologies on their property or use them only partially. The objective of this study is to investigate the farmers' perception about the use of PAT and determine which factors influence adoption of PAT. To this end, 133 soy producers from different states of Brazil were interviewed. Multiple logistic regression was used to build the estimated model (MA2). Education level, producer experience, planted area with maize, and government agencies as a source of information have a statistically significant influence on the adoption of PAT.

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

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

Investigations of precision agriculture technologies with application to developing countries

Investigations of precision agriculture technologies with application to developing countries

What if precision agriculture is not profitable?: A comprehensive analysis of the right timing for exiting, taking into account different entry options

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

IPM Essentials: Combining Biology, Ecology, and Agriculture for Sustainable Pest Control

Integrated Pest Management (IPM) represents a paradigm shift in pest control, moving away from heavy reliance on chemical pesticides to a more sustainable, environmentally friendly approach. This article explores IPM, an ecosystem based strategy that integrates biological, ecological, and agricultural sciences to achieve longterm pest control in agriculture. IPM emphasizes understanding pest life cycles and their interaction with the environment, utilizing a combination of techniques including biological control, cultural practices, mechanical and physical barriers, and targeted chemical interventions. Regular monitoring and informed decision making form the crux of this approach, focusing on economically viable and environmentally responsible pest control methods. The article highlights various success stories, the challenges faced in implementing IPM, and future directions including the incorporation of precision agriculture technologies and genetic advancements. Overall, IPM emerges as a crucial element in sustainable agriculture, promising to maintain ecological balance while ensuring effective pest management and provides an in depth examination of Integrated Pest Management (IPM), a multifaceted approach to sustainable pest control that synergizes biology, ecology, and agricultural science. IPM represents a paradigm shift from traditional, chemically intensive pest control methods to a more holistic, environmentally conscious framework. The core of IPM lies in understanding the life cycles and ecological interactions of pests, employing a diverse array of strategies including biological control through natural predators, cultural practices like crop rotation, mechanical and physical barriers, and judicious use of chemical pesticides. The article underscores the importance of regular monitoring and decision making based on established thresholds to maintain an effective, economically viable, and ecologically responsible pest management system. Case studies highlighting the successful implementation of IPM in various agricultural settings are discussed, alongside the challenges and prospects of IPM, particularly in the context of climate change and technological advancements. The article concludes that IPM is not only essential for sustainable pest control but also pivotal in ensuring long term agricultural productivity and environmental health.

Nurturing Crops, Enhancing Soil Health, and Sustaining Agricultural Prosperity Worldwide through Agronomy

Agronomy is the science and technology of crop and soil management for producing food, fiber, feed, and fuel. As the global population continues to rise, agronomy will play an increasingly vital role in meeting escalating food demands while sustaining natural resources. This review highlights major advancements in agronomic research and practice that enhance crop yields, soil health, and agricultural sustainability worldwide. Key topics include plant breeding for improved crop varieties, optimized plant nutrition and soil fertility management, efficient water management, integrated pest management (IPM), conservation agriculture techniques, precision agriculture technologies, and climate-smart farming approaches. Modern agronomy strategies such as drought-tolerant cultivars, micro-irrigation, reduced tillage systems, site-specific input applications, and integrated cropping systems can significantly improve productivity and resilience. However, continued innovation and diffusion of agronomic knowledge is crucial to nourish growing populations while protecting environmental quality. Collaborative efforts among researchers, educators, policy makers, and farmers will be imperative to apply advanced yet context-specific agronomic solutions that sustain agricultural prosperity into the future.

Application of IoT wireless technology in precision agriculture by the example of creating a smart greenhouse

Intelligent agricultural applications are gaining momentum, promising 24-hour monitoring of soil and crops, equipment productivity, storage conditions, plant and animal behaviour, energy consumption levels, etc. Combining different sensors, connected devices and agricultural facilities, IoT platform optimizes the development of intelligent agricultural systems and provides maximum flexibility, for example, for individual architectural design. This is a huge advantage for companies or farmers that plan to steadily expand their ecosystem of the IoT devices and over time introduce new intelligent agricultural solutions. Managing several solutions and upgrading them on a single IoT platform ensures rational operation and predictable results. One of such intellectual solutions is offered in this article on the example of the project "Smart greenhouse" using wireless IoT technologies.

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.

Enhancing Sustainable Crop Production through Innovations in Precision Agriculture Technologies

Precision agriculture technologies provide innovative tools to optimize crop production while minimizing environmental impacts. This review examines recent advances in precision ag systems to enhance sustainable agriculture. Key innovations include: remote and proximal crop sensing techniques leveraging hyperspectral imaging, thermal imaging, and Lidar to assess crop health and stress status; variable rate technologies like targeted sprayers and precision planters to reduce input waste; data analytics and decision support systems that integrate multi-source data streams to guide site-specific intervention; robotics and automation for precision field operations; and advanced breeding techniques and genomic tools enabling development of stress resilient, high yielding varieties. Adoption barriers, future technology trajectories, and priority research needs are discussed to further advance precision solutions that support productivity, efficiency, and sustainability goals.