Precision Agriculture Techniques Research Articles

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.

Drone Technology in Agricultural Practices: A SWOC Analysis

Drone technology, also known as Unmanned Aerial Systems (UAS), is transforming modern agriculture by introducing precision farming techniques that enhance crop monitoring, resource management, and sustainability. By providing real-time data on plant health, soil conditions, and irrigation needs, drones enable farmers to make informed decisions that optimize yields while reducing input costs. In India, the rapid growth of the drone sector highlights its increasing importance in addressing challenges such as labor shortages, water scarcity, and the need for more sustainable farming practices. This case study explores the adoption and impacts of drone technology in agricultural practices within Coimbatore District, Tamil Nadu, India. The purposive sampling method was used to chosen the farmers. Data were collected through a structured questionnaire and interviews with farmers who had adopted in drone technology. The study investigates the profile characteristics of farmers and evaluates the Strengths, Weaknesses, Opportunities, and Challenges (SWOC) related to the adoption of drones in agriculture. Findings revealed that drone usage significantly improved farm efficiency, reduced labor dependency, and optimized the application of fertilizers and pesticides, resulting in lower input costs and more sustainable practices. However, challenges such as high upfront costs, fragmented land holdings, technical expertise gaps, and limited drone availability constrained wider adoption. This research concludes that while drones offer substantial potential to modernize agriculture and address labor and environmental challenges, further government support and farmer training are crucial to overcome adoption barriers. The study underscores the role of drones in promoting climate-resilient and sustainable farming practices.

Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases

Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests and diseases, and guiding site-specific interventions. Key technologies discussed include remote sensing, proximal sensing, machine learning, robotics, and Internet of Things (IoT) sensors. The article highlights the potential of these technologies to improve the timeliness, accuracy, and efficiency of pest and disease detection while reducing reliance on chemical inputs. It also discusses the challenges and opportunities for integrating these technologies into current agricultural practices and extension services. The review concludes with recommendations for future research and development to advance precision agriculture solutions for sustainable crop protection.

Assessing the impact of financial resources on Sorghum yield in Al-Qadarif State, Sudan

The sorghum agricultural sector in Al-Qadarif State, Sudan, plays a pivotal role in ensuring regional food security. However, its productivity is hindered by significant constraints. Substantial improvements in sorghum yield are impeded by the intricate complexities that characterize this sector's financial resources, and agricultural practices. This study in Al-Qadarif State, Sudan, explores the relationship between financial resources and sorghum agrarian productivity. Data were collected from 200 sorghum farmers using standardized questionnaires during the 2022 season, and regression analysis was employed to investigate the association between financial factors and sorghum productivity. The findings indicate that financial accessibility considerations significantly influence sorghum productivity. This emphasizes the importance of improved financial resources for farmers to adopt innovative farming technologies and practices, leading to higher sorghum productivity levels. The study recommends the integration of digital financial services and precision agriculture technologies as a creative solution to enhance financial accessibility and sorghum productivity. By leveraging mobile money platforms and digital credit services, farmers can conveniently access financial resources, enabling them to invest in modern agricultural inputs and technologies. Additionally, the adoption of precision agriculture techniques, such as soil moisture sensors, drone-based crop monitoring, and variable rate application of inputs, can optimize resource utilization, increase yield, and reduce production costs. The combination of these innovative approaches has the potential to bridge the financial and knowledge gaps, promoting the widespread adoption of sustainable agricultural practices. This synergistic approach could significantly improve sorghum productivity, enhance farmer livelihoods, and contribute to regional food security.

Maize biomass yield variations due to diverse nitrogen applications using Aquacrop

AbstractIn the quest to increase crop yields and production intensity to meet the demands of a growing global population, the responsible use of nitrogen (N) applications is paramount. Excessive application of these fertilizers results in economic losses, environmental pollution and reduced N‐use efficiency. To address this challenge, precision agriculture techniques offer promising solutions. This study explored the relationships among applied N, maize crop parameters and the nitrogen nutrition index (NNI) to optimize N application usage and crop performance. The AquaCrop model was used to simulate water productivity and plant parameters accurately. The research establishes equations between the NNI and maize parameters, enabling the use of AquaCrop to simulate crop responses to varying N levels. The results demonstrate that AquaCrop effectively simulates more than 95% of the biomass changes. The findings suggest that applying N based on presented equations optimized the use of N applications, thereby mitigating economic losses and environmental impacts.

Development and Evaluation of a Variable-Rate Fertilizer Distribution System for Coffee Plants

Currently, Brazilian coffee farming seeks the rational use of resources through sustainable practices. As a result, the development of machinery with more efficient input application systems and the adoption of precision agriculture techniques have been yielding excellent results. This study was divided into two stages, with the first involving the adaptation of a solid fertilizer application machine with fixed doses, allowing dose variation using an electronic controller. The second stage consisted of conducting trials and their applications under operational conditions. The results confirmed that the developed system remained stable in terms of variable-rate fertilizer distribution for coffee cultivation. The machine’s lateral fertilizer distribution range met the demands of coffee farming satisfactorily. In field conditions, the developed system exhibited an average error of −2.9%, compared to the programmed doses, validating the accuracy of the machine and its suitability for use in coffee plantations.

Optimizing Olive Harvesting Efficiency Through UAVs and AI Integration

Olive harvesting, integral to global agriculture, faces challenges with traditional gathering methods which are labor-intensive and limited in precision, highlighting the need for innovative approaches. The study explores the application of Unmanned Aerial Vehicles (UAVs) in olive harvesting, with the aim to enhance efficiency and sustainability. Traditional methods, employing wood rakes, sticks, and rudimentary mechanical devices, pose challenges with high labor intensity and potential tree damage. UAVs present a transformative solution, offering aerial surveying, high-resolution imaging, and harvest timing data collection. This study evaluates existing olive harvest technologies, illuminating the superior efficiency of UAVs in minimizing damage and maximizing yield. The Potohar region in Pakistan exemplifies one area of intensive olive production, emphasizing the need for precision Agri-techniques. This study introduces the conceptual design of olive harvesting using drones, demonstrating its potential to revolutionize traditional practices, provide real-time monitoring, and enhance environmental sustainability. Results indicate substantial reductions in non-harvestable fruit and wood damage with drone-based harvesting, underscoring its promise for increased yield and reduced environmental damage. In conclusion, this study advocates for the adoption of precision agriculture techniques, integrating Artificial Intelligence and aerial technologies, to propel the olive industry's productivity and environmental impact forward.

Irrigation Water Management and Food Security in Nigeria

Food security remains a persistent challenge in Nigeria, a country with a rapidly growing population and a heavy reliance on rain-fed agriculture. Effective irrigation water management is crucial to improving agricultural productivity and addressing this critical issue, particularly in the face of climate change and increasing water scarcity. This study investigates the current state of irrigation water management and its impact on food security in Nigeria. The research employs a mixed-methods approach, combining an extensive literature review with field surveys, interviews, and focus group discussions with key stakeholders, including farmers, water resource managers, policymakers, and subject matter experts. The findings reveal significant gaps in Nigeria's existing irrigation water management system, including outdated infrastructure, inefficient water distribution, limited access to irrigation technology, and poor coordination among relevant government agencies and agricultural extension services. The study explores the multifaceted relationship between irrigation water management and food security, highlighting the impact of water availability, quality, and accessibility on crop yields, livestock production, and overall agricultural output. The analysis also examines the socioeconomic and environmental implications of poor irrigation water management, such as reduced farm incomes, food price volatility, and environmental degradation. Based on the research findings, the study proposes a comprehensive framework for sustainable irrigation water management in Nigeria. This framework emphasizes the integration of technological innovations, policy reforms, institutional strengthening, and community-based approaches to enhance the efficiency, equity, and resilience of the irrigation system. Key elements of the framework include modernizing irrigation infrastructure, promoting precision agriculture techniques, improving water governance and stakeholder coordination, and enhancing the capacity of farmers and water user associations. The implications of this study are far-reaching, as it provides policymakers, development agencies, and agricultural practitioners with a thorough understanding of the critical role of irrigation water management in achieving food security in Nigeria. The proposed framework offers a roadmap for the implementation of sustainable irrigation practices, which can contribute to increased agricultural productivity, improved rural livelihoods, and enhanced national food security. The findings of this research also offer valuable insights for other developing countries facing similar challenges in the realm of irrigation water management and food security.

The Mechanization Trend and Economic Prospects of Micro Irrigation in India

Agriculture plays a crucial role in the Indian economy, contributing nearly 16 percent to the total GDP, with farm mechanization serving as the backbone of agricultural GDP. This study analyzes the growth trend in agricultural mechanization in India by examining the shift from traditional human and animal-powered equipment to mechanically powered farm equipment like tractors and power tillers, which are vital for farm mechanization. Secondary data from 2004-2005 to 2021-2022 were used, and the compound annual growth rate (CAGR) and correlation analyses were performed to assess trends. The study found a 6.78 percent annual growth in tractor purchases, with a linear trend equation showing a 0.93 coefficient of determination (R²) at a 1 percent significance level, and a Pearson correlation coefficient of 0.76, indicating a strong correlation between tractor and power tiller purchasing trends. Precision farming, a type of farm management that uses information technology to optimize crop and soil health and productivity while minimizing production costs, is increasingly adopted due to advancements in mechanization. In India, precision farming techniques like drip and sprinkler micro-irrigation, covering 13,476,804 hectares with 46.90 percent under drip and 53.09 percent under sprinkler, have proven beneficial, especially in water-scarce areas. Precision farming is practiced mainly by medium to large progressive farmers, often experimentally or commercially, and offers profitable returns when applied to high-value crops such as fruits, vegetables, flowers, and medicinal plants.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.

Revolutionizing Agriculture: A Comprehensive Review of IoT Farming Technologies

IoT technology has triggered a revolutionary transformation across various industries, with agriculture being no exception. Smart farming, the integration of IoT in farming, has led to a complete overhaul of traditional agricultural practices by seamlessly combining sensor networks, data analytics, and automation. This comprehensive review aims to explore the diverse contributions of IoT in agriculture, synthesising insights from a wide range of research papers. The analysis delves into the multifaceted applications of IoT in farming, assessing its profound impact on productivity, resource management, environmental sustainability, and the challenges faced during implementation. By merging advanced sensor networks with data analytics, IoT in agriculture has given rise to intelligent farming practices, empowering farmers to make data-driven decisions and optimize their operations. Utilizing sensors to monitor soil moisture, temperature, and nutrient levels, along with advanced analytics, allows farmers to make real-time adjustments, thus maximizing crop yield and quality. Resource management has also been greatly affected by IoT in agriculture. The adoption of precision agriculture techniques enables farmers to precisely administer water, fertilizers, and pesticides, minimizing wastage and reducing the environmental footprint of conventional agricultural practices. Efficient resource use enhances agricultural sustainability and contributes to cost reduction and increased profitability for farmers. Moreover, the integration of IoT technology in agriculture holds great promise for fostering environmental sustainability. Farmers can proactively detect early signs of pest infestations or diseases by deploying IoT-based monitoring systems, facilitating timely intervention and reducing the need for excessive chemical treatments. This environmentally friendly approach helps preserve biodiversity, minimize soil and water pollution, and promote eco-conscious agricultural practices. Despite the numerous advantages, IoT implementation in agriculture does pose particular challenges. Connectivity issues, data security and privacy concerns, and the initial high costs of IoT deployment are among the primary obstacles faced by farmers. Addressing these challenges requires collaboration among stakeholders, including researchers, policymakers, and technology providers, to develop sustainable solutions that can facilitate the broader adoption of IoT in agriculture. In conclusion, this review paper sheds light on the immense potential of IoT technology in transforming the agricultural landscape. Smart farming, through IoT integration, paves the way toward sustainable food production, increased productivity, and efficient resource management. However, overcoming the challenges and ensuring seamless IoT integration is vital to fully harnessing this groundbreaking technology's benefits in agricultureIoT technology has triggered a revolutionary transformation across various industries, with agriculture being no exception. Smart farming, the integration of IoT in farming, has led to a complete overhaul of traditional agricultural practices by seamlessly combining sensor networks, data analytics, and automation. This comprehensive review aims to explore the diverse contributions of IoT in agriculture, synthesising insights from a wide range of research papers. The analysis delves into the multifaceted applications of IoT in farming, assessing its profound impact on productivity, resource management, environmental sustainability, and the challenges faced during implementation. By merging advanced sensor networks with data analytics, IoT in agriculture has given rise to intelligent farming practices, empowering farmers to make data-driven decisions and optimize their operations. Utilizing sensors to monitor soil moisture, temperature, and nutrient levels, along with advanced analytics, allows farmers to make real-time adjustments, thus maximizing crop yield and quality. Resource management has also been greatly affected by IoT in agriculture. The adoption of precision agriculture techniques enables farmers to precisely administer water, fertilizers, and pesticides, minimizing wastage and reducing the environmental footprint of conventional agricultural practices. Efficient resource use enhances agricultural sustainability and contributes to cost reduction and increased profitability for farmers. Moreover, the integration of IoT technology in agriculture holds great promise for fostering environmental sustainability. Farmers can proactively detect early signs of pest infestations or diseases by deploying IoT-based monitoring systems, facilitating timely intervention and reducing the need for excessive chemical treatments. This environmentally friendly approach helps preserve biodiversity, minimize soil and water pollution, and promote eco-conscious agricultural practices. Despite the numerous advantages, IoT implementation in agriculture does pose particular challenges. Connectivity issues, data security and privacy concerns, and the initial high costs of IoT deployment are among the primary obstacles faced by farmers. Addressing these challenges requires collaboration among stakeholders, including researchers, policymakers, and technology providers, to develop sustainable solutions that can facilitate the broader adoption of IoT in agriculture. In conclusion, this review paper sheds light on the immense potential of IoT technology in transforming the agricultural landscape. Smart farming, through IoT integration, paves the way toward sustainable food production, increased productivity, and efficient resource management. However, overcoming the challenges and ensuring seamless IoT integration is vital to fully harnessing this groundbreaking technology's benefits in agriculture

Protected Cultivation of Horticultural Crops: A Comprehensive Review

Horticultural crops grown under protection are becoming more and more significant in modern agriculture, offering a number of benefits such as increased yields, better quality, and protection from pests and bad weather. This comprehensive review aims to demonstrate protected cultivation methods as they currently exist and explore their future applications in horticulture. The first part of the examination covers the many types of protected production structures, including high tunnels, shade houses, and greenhouses, along with their advantages and disadvantages. The significance of protected farming in tackling concerns associated with worldwide food security is further underscored by preserving crop productivity throughout the year and limiting dependence on seasonal variations. This review looks more closely at the effects of protected cultivation methods on the growth and development of horticultural crops. These methods include improved crop morphogenesis, precipitation control, and the optimisation of environmental parameters like temperature, humidity, and carbon dioxide levels. Furthermore, the utilisation of advanced technologies like hydroponics, aeroponics, and vertical farming in protected growing systems is examined and its capacity to optimise crop yield while minimising resource consumption is emphasised. The difficulties and limitations associated with adopting protected cultivation are also examined in the study, including the need for artificial inputs, energy requirements, and financial considerations. It talks about environmentally friendly and sustainable ways to reduce these problems and preserve ecological balance, like switching to organic farming methods and using renewable energy sources. Future trends and developments in protected cultivation are covered in detail in the study's final section. These include the application of artificial intelligence, the adoption of smart farming technologies, and the employment of precision agriculture techniques. Higher yields and higher-quality horticultural crop output could be the outcome of these developments, which further optimise resource utilisation, improve automation, and better crop monitoring and management.

Precision Cultivation of Vegetable Crops to Increase Productivity: A Review

Precision cultivation is an innovative agricultural approach that leverages advanced technologies and data-driven methodologies to enhance the productivity and sustainability of vegetable crops. The precision agriculture techniques, including remote sensing, soil moisture sensors, GPS-guided machinery and data analytics which use to optimize various aspects of vegetable farming. These technologies facilitate informed decision-making regarding planting schedules, irrigation management, fertilization strategies and pest control which leading to improved crop yields and resource efficiency. Precision cultivation offers the potential for reduced environmental impact by minimizing chemical inputs and maximizing land use efficiency. Case studies demonstrate the successful implementation of precision practices across diverse climatic and geographical contexts which highlighting significant increases in productivity and quality of vegetable crops. The findings suggest that embracing precision cultivation not only addresses the challenges of food security but also contributes to sustainable agricultural practices that align with global environmental goals. Future research directions and technological advancements will be essential to further refine these methods and expand their applicability to diverse vegetable crops and farming systems.

The Role of Smart Farming Technologies in Enhancing Crop Yields and Resource Efficiency: A Global Perspective

This research article explores the role of smart farming technologies in improving crop yields and resource efficiency from a global perspective. Utilizing a qualitative methodology involving literature review and library research, the study aims to examine the various smart farming technologies and their impact on agricultural productivity and sustainability. The literature review encompasses a wide range of sources, including scholarly articles, reports, and case studies, to provide a comprehensive understanding of smart farming technologies. By synthesizing existing research, the article identifies key technologies such as precision agriculture, Internet of Things (IoT) sensors, drones, and data analytics, and examines their applications in enhancing crop yields and resource management. The findings highlight the significant contributions of smart farming technologies in optimizing agricultural practices, minimizing resource wastage, and maximizing efficiency. Through precision farming techniques and real-time monitoring systems, farmers can make data-driven decisions regarding irrigation, fertilization, and pest control, leading to improved crop quality and higher yields. Moreover, the study discusses the potential challenges and barriers to the adoption of smart farming technologies, including cost, infrastructure, and digital literacy issues, particularly in developing countries. It also explores the importance of supportive policies, capacity building initiatives, and public-private partnerships in facilitating the widespread adoption of these technologies.

Impact of Agricultural Activities on Climate Change: A Review of Greenhouse Gas Emission Patterns in Field Crop Systems.

This review paper synthesizes the current understanding of greenhouse gas (GHG) emissions from field cropping systems. It examines the key factors influencing GHG emissions, including crop type, management practices, and soil conditions. The review highlights the variability in GHG emissions across different cropping systems. Conventional tillage systems generally emit higher levels of carbon dioxide (CO2) and nitrous oxide (N2O) than no-till or reduced tillage systems. Crop rotation, cover cropping, and residue management can significantly reduce GHG emissions by improving soil carbon sequestration and reducing nitrogen fertilizer requirements. The paper also discusses the challenges and opportunities for mitigating GHG emissions in field cropping systems. Precision agriculture techniques, such as variable rate application of fertilizers and water, can optimize crop production while minimizing environmental impacts. Agroforestry systems, which integrate trees and crops, offer the potential for carbon sequestration and reducing N2O emissions. This review provides insights into the latest research on GHG emissions from field cropping systems and identifies areas for further study. It emphasizes the importance of adopting sustainable management practices to reduce GHG emissions and enhance the environmental sustainability of agricultural systems.

SPATIAL ANALYSIS OF BROWN SPIDER MITE (Oligonychus punicae Hirst) DAMAGE IN AVOCADO USING SPATIAL ANALYSIS BY DISTANCE INDICES (SADIE)

Avocado (Persea americana Mill.) is a perennial crop that belongs to the Lauraceae family. This crop is affected by various pests, the most important of which is the spider mite Oligonychus punicae Hirst (Acari: Tetranychidae), also known as the brown avocado spider. The damage begins with reddish spots that spread throughout the leaf, eventually tanning the foliage and staining the fruit, resulting in its rejection in the market. As a result, understanding its spatial distribution is critical for growers to plan and implement integrated pest management programs effectively, reducing their economic and environmental impacts. This study used spatial analysis by distance indices (SADIE) to determine the spatial distribution of damage to the Hass avocado crop caused by brown spider mite populations in Coatepec Harinas and Donato Guerra, two municipalities in the State of Mexico, Mexico. Spatial behavior allows for the development of preventive or corrective control measures for the use of precision farming techniques. Spatial distribution maps were created using the geostatistical technique of ordinary Krigeing, and the mean and variance of damage caused by the brown spider mite in avocado were examined. The findings revealed a clustered spatial distribution with several aggregation centers that shift over time and correlate with the plots’ terrain and climate. No homogeneous pattern was found in any plot, indicating the need to continue this type of research to better understand concentration conditions and implement specific actions in the field.

Smart Agriculture Application in Rice Cultivation

Rice is a staple food crop that feeds a significant portion of the world's population. Ensuring sustainable and efficient rice production is crucial for global food security. In recent years, the integration of smart agriculture technologies has shown promise in improving various aspects of rice cultivation. This paper provides an overview of the key smart agriculture applications that can enhance rice cultivation practices. The review discusses the use of precision farming techniques, such as GPS-guided tractors and drones for site-specific management of inputs like fertilizers and pesticides. It also examines how sensor networks and Internet of Things (IoT) devices can monitor environmental conditions, soil fertility, and plant health to enable data-driven decision-making. Smart irrigation systems leveraging soil moisture sensors and weather forecasts are highlighted as a means to optimize water usage. Furthermore, the paper explores the role of automation and robotics in automating labor-intensive tasks like transplanting, weeding, and harvesting. It also discusses the potential of machine learning and predictive analytics to improve crop yield forecasting, pest and disease management, and supply chain logistics. The adoption of these smart agriculture technologies in rice farming has demonstrated improvements in productivity, resource efficiency, and environmental sustainability. However, challenges remain in scaling up these solutions and ensuring their accessibility to smallholder farmers. The paper concludes by outlining future research directions and policy considerations to further advance smart agriculture in the rice cultivation sector.

Trends of Nanobiosensors in Modern Agriculture Systems.

Sustainable agriculture and the provision of food for all become dependent on the availability of efficient diagnostic techniques for the prompt identification of plant diseases. Current scientific findings suggest that nanotechnology can positively affect the agrifood industry by reducing the adverse effects of agricultural practices on human health and the environment, increasing food security and productivity, and fostering social and economic justice. Nanomaterials' unique physical and chemical characteristics have made it possible to employ them as cutting-edge, effective diagnostic instruments for various plant infections and other significant disease biomarkers. By creating diagnostic instruments and methods, nanobiosensors significantly contribute to the revolution of farming. In real time, nanobiosensors can detect infections, metabolites, pesticides, nutrient levels, soil moisture, and temperature. This helps with precision farming techniques and maximises resource use. To better address agricultural concerns, we have included the most recent research on the concept, types, applications, commercial aspects, and future scope of nanobiosensors in this review.

Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium-infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium-infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.