Precision Agriculture Technologies Research Articles

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Drone Technology for Precision Agriculture: Advancements and Optimization Strategies

This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.

GSE-YOLO: A Lightweight and High-Precision Model for Identifying the Ripeness of Pitaya (Dragon Fruit) Based on the YOLOv8n Improvement

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

Farmers’ adaptation to climate change in Southern Issyk-Kul

Agriculture is a key sector for people’s livelihoods in Kyrgyzstan, which highlights the role of local people’s perceptions of agricultural practices and optimisation of land use management. The purpose of this study was to assess the understanding of land use practices by local residents in four districts of the Southern Issyk-Kul. The level of awareness of sectoral issues was investigated by randomly selected farmers. The study found that modern farmers are aware of the existing problems and their contribution to the decline in agricultural productivity and increased vulnerability to environmental change. It was generalised that the solution of the problem under study requires practical interaction between government agencies and farmers aimed at providing the necessary information and resources for adaptation to climate-oriented agricultural practices. The potential of innovative precision farming technologies in the context of the studied issues was analysed. An important problem of the lack of a mechanism for the exchange of agricultural knowledge between farmers was identified, as it is difficult for them to determine where and from whom to gain experience. It was substantiated that the integration of agricultural consulting elements into the environment of farm operation has the potential to significantly stimulate the positive dynamics of agricultural development. Such an approach would allow for the most accurate selection of optimisation measures, which would affect the economic efficiency and environmental performance of farming. The practical value of the results of the study is seen in the possibility of using them to mitigate the process of adaptation of agricultural production in developing countries to inevitable climate change

Factors Affecting Precision Agriculture Technologies Adoption in Hong Kong

Factors Affecting Precision Agriculture Technologies Adoption in Hong Kong

Stimulating awareness of Precision Farming through gamification: The Farming Simulator case

Precision Farming (PF) provides different solutions to assist the decision-making process on farms. Current PF technologies such as variable rate site-specific applications can bring financial benefits to farmers as well as environmental advantages. Increasing scientific research and an expanding number of PF products are supporting a growing interest in PF applications. However, the actual implementation of these technologies on farms in many cases remains low. Therefore, there is a need to disseminate and transfer knowledge about the positive aspects of PF. One of the ways to facilitate the adoption process of PF technologies is education and training among farmers and other interested stakeholders. This paper presents a case study using the computer game Farming Simulator as an educational tool for raising awareness about the topic in an engaging and enjoyable way. Two distinct downloadable content (DLC) versions were developed and implemented in the versions 2019 and 2022 of the game, respectively, each with a range of PF functionalities (automatic steering, variable rate applications, yield mapping among others). The PF DLCs have received positive feedback from students and scientists but also the general public. The growing number of downloads (3,661,069 in total for both DLC versions as of 15th November 2023) demonstrates the effectiveness of computer games as an educational tool to educate and inform stakeholders (farmers, scientists, students, and the general public) about agricultural challenges and the potential of PF as a solution.

The Role of Precision Agriculture Technologies in Enhancing Sustainable Agriculture

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from the Farm Accountancy Data Network (FADN) and Eurostat (year 2021). The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of EUR 35,941–EUR 71,883 and a 20% and 15% reduction in the cost of crop protection and fertilizer usage, respectively, without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using Net Present Value (NPV) showed a positive value for economic farm sizes of EUR 100,000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. This is also an opportunity to meet the goals of the European Union Green Deal of minimizing emissions that cause climate change.

Recent Innovation in Precision Agriculture and their Impact on Crop and Soil Health: A Compressive Review

The maintenance of soil fertility and on-farm research or demonstrations might be enhanced by precision agricultural technologies. Using state-of-the-art technology, precision agriculture boosts agricultural output without negatively affecting the environment. Utilising cutting-edge technology and data analysis, precision agriculture aims to boost production, minimise waste, and maximise crop yields. This might be a viable approach to addressing some of the main problems facing modern agriculture, such feeding an expanding global population while lessening its impact on the environment. The application of precision agriculture starts with the collection of real-time data from various sources, such as satellite imagery, remote sensing, global positioning systems, geographic information systems, drones, soil sensors, and weather stations. Precision agriculture has become essential in addressing the challenges posed by a growing global population, climate change, and resource constraints. Recognising within-field variability and providing chances for differentiating treatment of sections within a field or industrial unit are what fuel demand for precision agriculture. Precision agriculture technology plays an important part in sustainable soil and crop management in modern agriculture by lowering crop production inputs and managing lands in an ecologically responsible way.

Revolutionizing Agriculture: Innovative Techniques, Applications, and Future Prospects in Precision Farming

Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimize the use of inputs, and enhance overall farm management practices. The key innovations in PA include the development and application of Geographic Information Systems (GIS) and Global Positioning Systems (GPS), which facilitate accurate mapping and variable rate technology (VRT) for site specific input management. Remote sensing technologies, encompassing both satellite imagery and UAVs (unmanned aerial vehicles), provide critical insights into crop health, soil conditions, and weather patterns, allowing for proactive and informed decision making. The integration of IoT in agriculture involves deploying sensors and connected devices to monitor soil moisture, temperature, and other environmental parameters in real time. This integration supports precision irrigation, climate monitoring, and efficient resource utilization. Big data analytics further enhances PA by processing vast amounts of data to generate actionable insights, enabling predictive analytics and decision support systems (DSS) that aid in optimizing farming operations. The article explores the applications of these advanced techniques in crop management, resource use optimization, and environmental stewardship. Examples include variable rate application of fertilizers, precision irrigation systems, and automated machinery such as drones and robotic harvesters. These innovations lead to significant improvements in crop yields, resource efficiency, and sustainability. Moreover, the review addresses the challenges associated with the adoption and implementation of PA technologies. These challenges include data management complexities, high initial costs, limited accessibility, and the need for technical expertise. The article discusses potential solutions such as cloud computing, machine learning algorithms, government subsidies, collaborative models, and comprehensive training programs to mitigate these barriers, the review highlights the integration of advanced technologies like artificial intelligence (AI), blockchain, and enhanced connectivity through 5G networks as pivotal developments that will further revolutionize precision agriculture. AI and machine learning will enhance predictive modeling and automated decision making, while blockchain will ensure transparency and traceability in supply chains. Enhanced connectivity will facilitate real time monitoring and collaborative platforms, driving efficiency and innovation in farming practices.

Farmers’ willingness to adopt precision agricultural technologies to reduce mycotoxin contamination in grain: evidence from grain farmers in Spain and Lithuania

PurposeThis study examines the willingness of Spanish and Lithuanian grain farmers to adopt a combined approach of preventive site-specific spraying (PSSS) and selective harvesting (SH), two precision agricultural technologies (below referred to as PSSS-SH) aimed at mitigating the risk of mycotoxin contamination in barley and wheat.MethodsData were collected from 190 commercial grain farmers using a choice experimental survey. The empirical analysis relied on the estimation of mixed logit and integrated latent class models.ResultsThe surveyed farmers were heterogeneous in their preference for the PSSS-SH technology, with a majority (81%) reporting that they were willing to adopt and pay for the PSSS-SH technology. Furthermore, the farmers’ willingness to adopt PSSS-SH technology was influenced by the trade-offs between the potential production, economic and environmental changes.ConclusionProfit maximization is not the only motivation for a farmer’s decision to adopt PSSS-SH, there are also important non-financial benefits that align with the observed choices. Furthermore, the perceived usefulness of the technology, the willingness and readiness to use the technology, and the farmer characteristics (e.g. cooperative membership, employment status, share of household income from grain production and past experience with precision farming technology) were positively associated with uptake of the PSSS-SH technology. Therefore, extension programmes should have a special focus on the perceived usefulness of the technology, the willingness and readiness of farmers to use it, and its unique characteristics.

Design and Fabrication of an Autonomous Rice Transplanter

The goal of this study was to create a prototype of an automatically guided rice transplanter using a Pixhauk flight controller, 3DR telemetry, Raspberry Pi, Tof sensor, gyro sensor, and other GPS navigation sensors to determine the field performance of that machine. GPS was used to obtain data such as location, direction, and speed, whereas machine vision can provide a navigation line. The next position of the transplanter can be determined using feature points extracted from the navigation line. The controller area network bus complies with the actuator control command and data communication protocols. Electrical actuators control steering and transmission based on a vehicle's location in a field. The percentage of missing, buried, and floating hills was observed to be 1.6%, 1.4%, and 3.0%, respectively, as the maximum values were found in clay soil at T1. Over time, percentages of losses decreased, and the lowest percentage of losses was given. The lowest percentage of missing, buried, and floating hills was found at 0.8%, 0.2%, and 0.8%, respectively, in clay loam soil at T5. Planting efficiency improved, and losses decreased as treatment levels upgraded. Clay loam soil in T5 had the highest planting efficiency percentage (88%), whereas clay soil in T1 had the lowest (61%) proportion. The findings indicated that T4 and T5 enhanced rice output by decreasing the losses incurred during seedling transplantation. The study suggested that the developed rice transplanter might be suitable for planting rice seedlings to establish meaningful mechanization through precision agriculture technology.

Revolutionizing Farm Management with Modern Agricultural Extension Techniques: A Review

The transformative impact of modern agricultural extension techniques on farm management in India, highlighting the integration of digital technologies, precision agriculture, and innovative management systems to enhance productivity and sustainability. Traditional farming practices in India, characterized by low efficiency and productivity, are undergoing significant changes with the introduction of technologies such as mobile apps, remote sensing, GIS, and sensor-based systems. These technologies facilitate improved crop monitoring, resource mapping, and data-driven decision-making, leading to enhanced crop yields, resource efficiency, and economic benefits. Notably, precision agriculture technologies like automated systems and IoT have enabled real-time, precision farming practices that significantly reduce resource waste and increase crop productivity. However, the adoption of these modern techniques is not without challenges. Issues such as accessibility, the digital divide, economic constraints, and the need for continuous training hinder widespread implementation. The review also discusses policy recommendations for supporting the adoption of these technologies, including increased government investment in infrastructure, regulatory frameworks, and capacity building. The integration of these modern techniques with global agricultural trends-especially those aimed at sustainability and climate change adaptation-is crucial for the future of Indian agriculture. By leveraging advanced technologies and aligning with global standards, India can not only enhance its agricultural productivity but also improve the socio-economic status of its rural population, contributing to broader economic and environmental goals.

Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review

Precision agriculture (PA) technologies have spearheaded a revolution in contemporary farming practices, ushering in an era of sustainability, efficiency, and data-driven agricultural production. This comprehensive examination aims to delve deeply into the intricate dynamics surrounding the adoption of precision agriculture technologies among farmers, synthesizing existing literature, identifying key determinants, barriers, and implications. The adoption of precision agriculture technologies is influenced by a myriad of factors spanning socio-economic, institutional, technological, psychological, and attitudinal dimensions. Socio-economic factors such as farm size, income level, access to credit, and demographic characteristics are pivotal in shaping farmers' decisions to adopt PA technologies. Institutional factors, including government policies, extension services, market structures, and support networks, also wield significant influence over adoption patterns. Technological considerations, such as the complexity, compatibility, and perceived benefits of precision agriculture technologies, impact farmers' adoption decisions. Moreover, psychological factors such as risk perception, innovativeness, and attitudes towards change contribute to shaping farmers' willingness to embrace new technologies. The adoption of precision agriculture technologies holds immense promise for enhancing farm productivity, sustainability, and profitability. By enabling precise management of inputs such as water, fertilizers, pesticides, and seeds, PA technologies help optimize resource use, minimize environmental impact, and maximize yield potential. Furthermore, the integration of advanced data analytics, remote sensing, and IoT (Internet of Things) devices facilitates real-time monitoring, decision-making, and predictive analytics, empowering farmers with actionable insights for improved farm management. However, despite the compelling benefits, the adoption of precision agriculture technologies faces several barriers and challenges. High initial investment costs, limited access to capital, inadequate infrastructure, and technical knowledge gaps pose significant hurdles for farmers, particularly those operating in resource-constrained settings. Concerns regarding data privacy, security, and ownership also contribute to farmers' apprehensions about adopting PA technologies, raising questions about data governance and regulatory frameworks. To overcome these barriers and promote the widespread adoption of precision agriculture technologies, concerted efforts are needed from various stakeholders. Farmer education and training programs play a crucial role in enhancing digital literacy, technical skills, and awareness about the potential benefits of PA technologies. Government policies and incentives, including subsidies, grants, and tax incentives, can help alleviate financial barriers and incentivize adoption. Moreover, collaboration between researchers, industry stakeholders, and policymakers is essential to drive technological innovation, develop user-friendly solutions, and address emerging challenges. Investments in infrastructure, such as rural broadband connectivity and sensor networks, are also imperative to ensure the seamless integration and scalability of precision agriculture technologies across diverse farming landscapes.

A Brief Overview of Technologies in Automated Agriculture: Shaping the Farms of Tomorrow

As global population continues to grow, there is an increasing need for innovative solutions to enhance agricultural productivity, efficiency, and sustainability. To meet increasing population demand, agricultural production must be doubled. The global population is projected to rise by almost two billion individuals within the next three decades. With global challenges such as population growth, climate change, and resource formation, automation in farming practices is one of the key driving forces behind this revolution. Robotics, coupled with artificial intelligence (AI) and advanced data analytics, are transformative solutions for precision farming and smart farming technologies. These technologies enable continuous and efficient farming operations and provide detailed monitoring at a plant-by-pant level, optimizing resource use and reducing the environmental footprint. Technological advancements have led to the development of various robotic systems, including agricultural grippers and autonomous machinery, which are integral to the automation of farming tasks, from sowing to harvesting. However, the adoption of such technologies is not without challenges. High initial investment costs, connectivity issues, and data security are some of the barriers that need to be addressed. The potential benefits of reduced operational costs, improved crop quality, and enhanced farm output make it a promising solution for the future of farming. In this article, we discuss the multifaceted role of robotics in modern agriculture by exploring both technological advancements and challenges to widespread adoption.

A CRITIC-CODAS Approach for Ranking WirelessCommunication Technologies in Precision Agriculture: Application to Irrigation System

This study evaluates and ranks wireless communication technologies for agricultural irrigation systems using the CRITIC-CODAS multi-criteria decision-making method. Technologies including WSN, LPWAN, LoRa, 5G, IoT, and NB-IoT are compared based on range, power consumption, data rate, coverage, latency, battery life, and device density. The CRITIC method is used to objectively determine criteria weights, with data rate, latency, and battery life emerging as the most important factors. 5G ranks highest with its superior data rate and device density, but its high-power needs may limit suitability for remote agriculture. IoT offers a balanced option across multiple criteria, while WSN excels in power efficiency and battery life. LPWAN, LoRa, and NB-IoT provide excellent range and battery life for wide-area, low-power applications despite lower data rates. The analysis provides a framework for stakeholders to select appropriate technologies based on specific agricultural requirements. Matching wireless technologies to irrigation needs can enhance agricultural sustainability and productivity. Further research could explore technology combinations, cost factors, and real-world testing. This systematic evaluation approach can be extended to other smart agriculture applications to optimize technology adoption.

Hazelnut Cultivation in the Face of Climate Change: A Review

Hazelnut (Corylus avellana L.) cultivation faces substantial challenges in the wake of climate change. This review synthesizes findings from various studies to examine the impacts of climate change on hazelnut cultivation, strategies for mitigating these impacts, and the potential role of hazelnut orchards as carbon sinks. Examined are hazelnut trees' physiological responses to shifting climatic conditions, management approaches to bolster resilience and productivity, and the carbon sequestration potential of hazelnut orchards. Additionally, the role of fertilization, irrigation, and other agricultural practices in influencing hazelnut growth and yield under changing climates is evaluated. By integrating sustainable agricultural practices and leveraging precision agriculture technologies, hazelnut growers can improve environmental sustainability and economic viability. This review provides comprehensive insights and practical recommendations for sustaining hazelnut production in the face of climate change.

Are Indonesian rice farmers ready to adopt precision agricultural technologies?

Are Indonesian rice farmers ready to adopt precision agricultural technologies?

Extension Experts‘ Intentions to use Precision Agricultural Technologies, a Test with the Technology Acceptance Model

Abstract Precision agriculture (PA) is a farm management strategy that relies on various technologies to improve the productivity and sustainability of farming operations. The adoption of PA entails on-farm and off-farm benefits; however, the adoption rates remain low in Iran. Using the socio-psychological framework of the technology acceptance model (TAM), this study examined agricultural extension experts‘ intentions to use precision agricultural technologies (PATs) in Ardabil province, Iran. Structural equation modelling (SEM-PLS) was used to map the components of the TAM (perceived usefulness, perceived ease of use, and attitudes toward PATs). All the components of the TAM showed a significant effect on experts‘ intentions, confirming the importance of socio-psychological variables in predicting agricultural experts‘ decision to apply PATs. Experts perceived PATs as helpful and relatively easy to use. In addition, they had positive attitudes toward PATs and intended to use most PA technologies. The TAM posits that two attitudinal components of perceived usefulness (PU) and perceived ease of use (PEU) determine acceptance and use. PU is the degree to which one believes using technology would enhance job performance, while PEU is the degree to which using technology is free of effort. The results showed that PEU and PU had a positive impact on attitudes. The three constructs positively affected behavioural intention toward the application of PATs and explained 68.8% of the variance of this construct. Due to the novelty of PA in the country, PEU was the most critical determinant of intention.

Discovering farmers’ views of on‐farm precision experimentation

AbstractPrecision agriculture technologies (PATs) have revolutionized agriculture production and provide many benefits to farmers. Among these benefits is the ability to conduct experiments using PATs and collaborate with researchers in a process known as on‐farm precision experimentation (OFPE). OFPE is a citizen‐science approach that fosters relationships and knowledge‐sharing to address challenges of mutual interest. While the literature on precision agriculture is extensive, little research has addressed farmers’ willingness to conduct OFPE with researchers. Interviews with 11 Illinois farmers revealed high adoption rates of PATs. Interest in collaborating with researchers to conduct OFPE was mixed, with farmers identifying clear channels of communication as the most important factor in their decision to collaborate. While additional study is needed among the larger farming community, these initial insights may contribute to the larger effort to encourage greater collaboration between researchers and farmers to find real‐world solutions to agronomic, economic, and environmental challenges.Core Ideas Precision agriculture technologies (PATs) allow farmers to engage in on‐farm precision experimentation (OFPE). OFPE fosters farmer and researcher collaboration to help address agronomic challenges. This study provides initial insight into farmers’ perceptions of and interest in OFPE.

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.