Precision Agriculture Practices Research Articles

Uncertainty-based fuzzified environmental-socio-economic risk assessment of precision agricultural practices

Uncertainty-based fuzzified environmental-socio-economic risk assessment of precision agricultural practices

Climate Smart Agriculture: Innovating Sustainable Practices for a Changing Climate

Climate Smart Agriculture (CSA) is a strategy aimed at lowering greenhouse gas emissions, strengthen climate change resilience, raising crop production and incomes significantly. In a variety of agro-ecological zones, the implementation of climate smart agriculture methods has significantly improved the availability of food, adaptability to fluctuations in the climate, and emissions reduction. One of the key benefits of CSA is its ability to improve agricultural productivity and stabilize food security, particularly for small-scale farmers in developing nations who are most vulnerable to climate change. By integrating sustainable techniques such as conservation agriculture, crop diversification, and agroforestry, CSA helps ensure stable yields, even amid erratic weather patterns. Enhanced soil fertility, enhanced irrigation practices and the use of varieties of crops that are climate resilient all contribute to sustained productivity, mitigating the risks of crop failure and food shortages. CSA also strengthens agricultural systems' resilience to climate impacts by promoting practices like zero tillage, residue management, and efficient irrigation, which improve soil health and water retention, making crops more resistant to droughts and floods. In addition to boosting resilience, CSA contributes to climate change mitigation by reducing agriculture's carbon footprint. Precision farming, organic agriculture, and other low-emission practices help curb greenhouse gas emissions from farming activities. Mobile technology is also emerging as a key driver of CSA by providing real-time information, decision-making support, and access to essential resources, further enhancing the adaptability and sustainability of farming systems. The significance of CSA as a strategic solution to the threats that climate change poses to food security and agricultural sustainability is being more widely acknowledged by governments. As a result, numerous initiatives have been launched at national, regional, and international levels to encourage the widespread adoption of CSA practices. In order to achieve sustainable agricultural development and ensure food security in the face of climate change, CSA must be scaled up around the world.

Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Easy yield mapping for precision agriculture

ABSTRACT Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture.

Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

A 3D-Printable smartphone accessory for plant leaf chlorophyll measurement

Plant health and nutrition are universally inferred from leaf chlorophyll content. This research developed a 3D-printed accessory which attaches to the ambient light sensor in a smartphone to estimate leaf chlorophyll content in five tropical plant species. It unveils 3D printing files and assembling details to freely built the accessory anywhere. It is made from a 3D-printed body, a lighting circuit and common spare parts to measure a 663 nm LED band transmission through intact plant leaves. This chlorophyll absorbing light band allows to measure its concentration. The device was tested by comparing its readings to the universal spectrophotometric test or by leaf parallel measurements with a standard SPAD 502™ meter, and it performed as well as these universal standard methods. Due to well-studied relationships between chlorophyll concentration and nutritional status of plants, and the ubiquitous presence of other sensors in smartphones today, the independent improvement and adoption of this smartphone-connected system would ease the spread of precision farming and digital agronomy practices throughout the different scales of agriculture.

Feature diffusion reconstruction mechanism network for crop spike head detection.

Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learning-based visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms. To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers. According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP@.5 of 94.23%, 75.13% and R 2 value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms. Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity.The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices.

Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.

Cover crop root exudates impact soil microbiome functional trajectories in agricultural soils

BackgroundCover cropping is an agricultural practice that uses secondary crops to support the growth of primary crops through various mechanisms including erosion control, weed suppression, nutrient management, and enhanced biodiversity. Cover crops may elicit some of these ecosystem services through chemical interactions with the soil microbiome via root exudation, or the release of plant metabolites from roots. Phytohormones are one metabolite type exuded by plants that activate the rhizosphere microbiome, yet managing this chemical interaction remains an untapped mechanism for optimizing plant-soil-microbiome interactions. Currently, there is limited understanding on the diversity of cover crop phytohormone root exudation patterns and our aim was to understand how phytochemical signals selectively enrich specific microbial taxa and functionalities in agricultural soils.ResultsHere, we link variability in cover crop root exudate composition to changes in soil microbiome functionality. Exudate chemical profiles from 4 cover crop species (Sorghum bicolor, Vicia villosa, Brassica napus, and Secale cereal) were used as the chemical inputs to decipher microbial responses. These distinct exudate profiles, along with a no exudate control, were amended to agricultural soil microcosms with microbial responses tracked over time using metabolomes and genome-resolved metatranscriptomes. Our findings illustrated microbial metabolic patterns were unique in response to cover crop exudate inputs over time, particularly by sorghum and cereal rye amended microcosms. In these microcosms, we identify novel microbial members (at the genera and family level) who produced IAA and GA4 over time. Additionally, we identified cover crop exudates exclusively enriched for bacterial nitrite oxidizers, while control microcosms were discriminated for nitrogen transport, mineralization, and assimilation, highlighting distinct changes in microbial nitrogen cycling in response to chemical inputs.ConclusionsWe highlight that root exudate amendments alter microbial community function (i.e., N cycling) and microbial phytohormone metabolisms, particularly in response to root exudates isolated from cereal rye and sorghum plants. Additionally, we constructed a soil microbial genomic catalog of microorganisms responding to commonly used cover crops, a public resource for agriculturally relevant microbes. Many of our exudate-stimulated microorganisms are representatives from poorly characterized or novel taxa, revealing the yet to be discovered metabolic reservoir harbored in agricultural soils. Our findings emphasize the tractability of high-resolution multi-omics approaches to investigate processes relevant for agricultural soils, opening the possibility of targeting specific soil biogeochemical outcomes through biological precision agricultural practices that use cover crops and the microbiome as levers for enhanced crop production.BPG5Z_iDvqR4KrAs2TcSTiVideo

Assessing Remote Sensing-Based Maize Crop Biophysical Characteristics and Evapotranspiration Estimations

The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms, Unmanned Aerial Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5 in assessing crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aims to evaluate the accuracy of vegetation indices (VIs) derived from these platforms in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional vegetation cover (Fc), as well as ETa. Field experiments were conducted during 2022 at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, U.S.A., using different irrigation strategies. Surface reflectance data collected using a handled sensor and observed LAI, Hc, and Fc values, served as ground truth for validating RS estimates. The study applied various statistical analyses to compare the performance of different RS platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of RS models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced RS technologies in enhancing precision agriculture practices and optimizing water resource management.

Role of Entomology in the Era of Precision Agriculture: A Review

Precision agriculture (PA) represents a transformative approach to farming by utilizing advanced technologies like GPS remote sensing, and data analytics to optimize crop production and minimize environmental impacts. While technological advancements in PA are well-documented, the role of entomology is often overlooked. This review highlights the critical intersection between entomology and PA, demonstrating how understanding insect behavior, population dynamics, and ecological interactions enhances PA practices. Insects play pivotal roles in pest management, pollination, and ecosystem health, making their study essential for developing effective PA strategies. The review examines how entomological research has informed precision pest management, integrated pest management (IPM) systems, and the use of biological control agents within the PA framework. It discusses how integrating entomological insights with PA technologies, such as remote sensing and sensor networks, enables real-time monitoring and targeted pest control, thus improving crop yields and reducing pesticide use. Additionally, the review addresses challenges such as data integration, technology accessibility, resistance management, and environmental concerns, and highlights prospects including advancements in technology, innovation in monitoring techniques, and the integration of biological control methods. By emphasizing the need for interdisciplinary collaboration, this review underscores the importance of combining entomological knowledge with PA tools to develop more sustainable and efficient agricultural practices. The integration of entomology into PA not only enhances pest management but also contributes to broader goals of environmental sustainability and agricultural resilience. Overall, this review offers a comprehensive overview of how entomology can advance precision agriculture and foster sustainable food production systems.

Crop Yield Prediction and Classification using the Agriculture Resources and Data Mining Techniques

This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Moreover, the integration of meteorological forecasting with agronomic models and geographical information systems (GIS) facilitates site-specific crop management and precision agriculture practices. By combining meteorological data with soil properties, crop phenology, and socio-economic factors, data mining techniques enable the development of predictive models that enhance crop yield potential and optimize resource allocation. By leveraging advanced data analytics and machine learning algorithms, agricultural stakeholders can make informed decisions, mitigate risks, and enhance productivity in the face of changing climatic conditions and environmental uncertainties.

Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images

Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and mAP@0.5 of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest mAP@0.5 in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.

Effects of different water stresses under subsurface infiltration irrigation on eggplant growth and water productivity

Soil moisture deficiency limits eggplant growth and affects the yield and quality. Identifying an appropriate deficit irrigation strategy to enhance irrigation water productivity is an urgent task. Additionally, the dynamic response of eggplants to water stress at various phenological stages needs further clarification. In this study, eggplants were used as experimental materials, with field capacity (FC) (Senyigit et al.) serving as the irrigation standard. Two levels of deficit irrigation treatments (60 % FC and 50 % FC) were applied at three key phenological stages of eggplant (seedling stage, flowering and fruiting, and prime fruit stage). A randomized block design was used with non-deficit irrigation (80 % FC) during the phenological stages as the control (W1), resulting in seven treatments, each with three replicates. The results showed that at the same phenological stage, plant height, stem diameter, leaf area index (LAI), dry matter accumulation, total fresh root weight, total root length, total root surface area, and total root volume decreased as the irrigation threshold was lowered. The content of vitamin C, soluble sugars, souble proteins, and crude fiber increased as the irrigation threshold decreased. Leaf photosynthetic parameters (photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci)) were positively correlated with the irrigation threshold. The W1 treatment had the highest yield (89.1 t·ha−1 in 2018; 88.8 t·ha−1 in 2019), followed by the W2 treatment (86.1 t·ha−1 in 2018; 85.6 t·ha−1 in 2019). Water deficit during the prime fruit stage (W6, W7) had the most significant impact on yield. W1 had the highest water productivity (19.4 kg·m−3 in 2018; 19.2 kg·m−3 in 2019), with no significant difference from W2 (19.4 kg·m−3 in 2018; 19.0 kg·m−3 in 2019). Using the Jensen function, the water sensitivity of eggplants at different phenological stages was ranked as follows:prime fruit stage > flowering and fruiting stage > seedling stage. Path analysis indicated that dry matter accumulation, single fruit weight, and single plant yield were important indicators affecting yield. Considering irrigation amount, eggplant growth, fruit yield and quality, and water productivity, the optimal deficit irrigation thresholds for eggplants in arid regions are determined to be 60 % FC during the seedling stage, 60 % FC during the flowering and fruiting stage, and 80 % FC during the prime fruit stage. The findings of this study offer valuable insights for precision agricultural irrigation practices.

Enhancing Rice Cultivation Efficiency in Tidal Lowland of Delta Saleh, Indonesia: Precision Farming Practices for Water Management and Soil Health Improvement

Tidal lowland is a marginal land characterized by low pH, deficient nutrients, and salinity. Despite these challenges, El Niño phenomenon often occurs during the second planting season, resulting in long droughts. However, tidal lowland must be used for cultivation due to the need for rice and the land should be treated accurately. Therefore, this research aimed to address the issues by improving the efficiency of rice cultivation on tidal lowland through precision farming practices. A survey and land analysis were conducted in tidal lowland of B typology in Delta Saleh, Indonesia, from March 2023 to June 2023. In this precision farming practice, water management was highly prioritized, starting from tertiary channels such as optimizing sluice gate operations and monitoring water levels in channels and groundwater. Additionally, pH, CEC, and C-Organic analysis were also carried out in rice cultivation, as showed by the equation Y = 0.15 - 0.001 pH + 0.000 CEC + 0.000 C-Organic. The highest production yield was 2.05 tons/ha in P5, with the SEW-10 value during cultivation activities being 778 cm and the number of days above -10 reaching 84. Moreover, the efficiency of rice cultivation was improved through precision agricultural practices by using valve sluices and levees.

Enhancing Rice Cultivation Efficiency in Tidal Lowland of Delta Saleh, Indonesia: Precision Farming Practices for Water Management and Soil Health Improvement

Tidal lowland is a marginal land characterized by low pH, deficient nutrients, and salinity. Despite these challenges, El Niño phenomenon often occurs during the second planting season, resulting in long droughts. However, tidal lowland must be used for cultivation due to the need for rice and the land should be treated accurately. Therefore, this research aimed to address the issues by improving the efficiency of rice cultivation on tidal lowland through precision farming practices. A survey and land analysis were conducted in tidal lowland of B typology in Delta Saleh, Indonesia, from March 2023 to June 2023. In this precision farming practice, water management was highly prioritized, starting from tertiary channels such as optimizing sluice gate operations and monitoring water levels in channels and groundwater. Additionally, pH, CEC, and C-Organic analysis were also carried out in rice cultivation, as showed by the equation Y = 0.15 - 0.001 pH + 0.000 CEC + 0.000 C-Organic. The highest production yield was 2.05 tons/ha in P5, with the SEW-10 value during cultivation activities being 778 cm and the number of days above -10 reaching 84. Moreover, the efficiency of rice cultivation was improved through precision agricultural practices by using valve sluices and levees.

Intelligent Farming Systems in Future Using Machine Learning: A Focus on India

Abstract: The integration of machine learning (ML) into Indian agriculture holds transformative potential for addressing critical challenges such as climate variability, water scarcity, soil degradation, and market fluctuations. This paper explores the current state of ML applications in Indian agriculture, highlighting successful case studies and initiatives led by government, private sector, and academic institutions. It discusses the technological integration of ML with the Internet of Things (IoT), remote sensing, and blockchain to enhance precision farming practices. Key barriers to widespread adoption, including data quality, infrastructure, and farmer awareness, are identified, along with strategies to overcome them. Future directions emphasize the importance of robust data infrastructure, localized ML models, collaborative research, sustainable practices, and supportive policy frameworks. By leveraging ML, Indian agriculture can achieve significant improvements in productivity, sustainability, and profitability.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

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.