Search
77 articles for “Machine Learning in Agriculture”
-
A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article
-
AI-Enabled Linear Regression Model for Spectroscopic Milk Adulteration Analysis
Abstract: Milk adulteration poses a serious threat to public health and quality assurance in the dairy industry. This requiring rapid, reliable, and non-destructive detection techniques. This study presents a linear regression-based analytical model for identifying and quantifying milk adulteration using spectroscopic data. Spectral measurements of milk samples, including both pure and adulterated variants were acquired using spectroscopic techniques at relevant wavelengths.Blending of other components in pure milk , is specifically called …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 15, Issue 1, 2026 · pp. 28–42 Read article
-
Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
-
Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges
Abstract: The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 88–96 Read article
-
Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management
Abstract: Artificial Intelligence (AI) is transforming entomology by enabling scalable, data-driven approaches to insect identification, ecological monitoring, and sustainable pest management. This review synthesizes recent global advances in AI applications across taxonomy, behavioral ecology, predictive modeling, and precision agriculture. Machine learning and deep learning techniques—including convolutional neural networks, acoustic classification models, and ensemble predictive algorithms—have demonstrated high classification accuracies (often exceeding 90% under controlled conditions) and improved early detection of pest …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 29–40 Read article
-
Farmer’s Pal
Abstract: Precision agriculture, characterized by data-driven decision-making, has transformed contemporary farming practices. To increase agricultural sustainability and efficiency, this abstract investigates the combination of sensor monitoring, machine learning, and picture processing. A network of sensors continuously collects vital environmental data, including temperature, humidity, rainfall, sunshine, soil moisture, and conductivity, for precision agriculture. By providing real-time insights, these sensors enable farmers to make informed choices about pest control, fertilization, and irrigation. This …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 19–31 Read article
-
AGRISMART: Crop and Soil Management System
Abstract: Agriculture has played a crucial role in developing countries where the majority of the rural population relies on it for their livelihoods. A finer-grade crop classification has become crucial in the context of precision agriculture. In recent years, the volume of open image data has grown significantly. This can be used in combination with machine learning techniques to classify crop types in the agricultural industry. The proposed crop species recognition …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 50–55 Read article
-
IoT-Based Adaptive Irrigation Solution for Smart Farming
Abstract: Facing escalating global challenges such as water scarcity and unpredictable weather conditions, precision farming emerges as a crucial solution for enhancing agricultural productivity and sustainability. This paper introduces a smart irrigation system which employs IoT-based technology, integrating sensors and a designed to enhance water efficiency and boost agricultural productivity. The system leverages live data related to soil moisture and temperature, cloud cover, and precipitation, coupled with a cloud-based infrastructure using …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 3, 2025 · pp. 17–24 Read article
-
AI and IoT in Sustainable Agriculture: A Review
Abstract: Artificial Intelligence (AI) and Internet of Things (IoT) integration have transformed the world of sustainable agriculture, presenting new ways of resource optimization, increasing crop yields, and making environmental sustainability more accessible. The current literature review analyzes the applications of AI and IoT in three significant agricultural systems: aquaponics, hydroponics, and poultry farming. By critically analyzing recent studies, this paper emphasizes how deep learning- enabled computer vision techniques allow for the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 2, 2025 · pp. 32–45 Read article
-
Farming Forward: Integrating IoT, AI, and Image Processing for Sustainable Agriculture
Abstract: Farming Forward: Integrating IoT, AI, and Image Processing for Sustainable Agriculture" explores the convergence of cutting-edge technologies in revolutionizing traditional farming practices towards sustainability. This study investigates the integration of Internet of Things (IoT), Artificial Intelligence (AI), and Image Processing techniques in agricultural contexts, aiming to enhance efficiency, productivity, and environmental stewardship. Through a comprehensive review of recent advancements and case studies, this research elucidates the transformative potential of IoT-enabled …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 52–69 Read article
-
Fertilizer Prediction Using Machine Learning
Abstract: Fertilizer prediction is a critical aspect of modern agriculture, aimed at optimizing resource utilization while maximizing crop yields. In recent years, machine learning (ML) techniques have emerged as powerful tools for addressing this challenge by leveraging data-driven approaches to predict the optimal type and quantity of fertilizer required for different crops and soil conditions. This research paper provides a comprehensive review of the existing literature and methodologies employed in fertilizer …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 2, 2024 · pp. 26–35 Read article
-
Revolutionizing Plant Disease Detection: A Comprehensive Review
Abstract: Rise in population demands more food production but the diseases in plants contribute to loss. The advancement in agricultural field has a remarkable effect in detecting plant diseases. These diseases will have a major impact on the quality of plant and yield and hence can destroy the entire plant if they are not controlled on time. To reduce disease-related losses, it is necessary to identify different types of diseases and …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 2, 2023 · pp. 44–55 Read article
-
Harvestify: ML Based Tool for Home Gardening and Farming
Abstract: This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system's architecture. The Crop Disease Detection module analyses …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 18–28 Read article
-
A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
-
Fertile Data: Advanced Strategies for Crop Optimization Through Machine Learning Processing
Abstract: The venture, titled "FertileData: Advanced Strategies for Crop Optimization Through Machine Learning processing" is created utilizing HTML, CSS, and JavaScript for the front conclusion, and Python for the back conclusion. In a nation like India, where a noteworthy parcel of the populace depends on agribusiness for their vocation, joining progressed advances such as Machine Learning and Profound Learning into cultivating hones can revolutionize the industry. This venture presents a user-friendly …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 2, 2025 · pp. 25–35 Read article
-
Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
-
Transformative Breakthroughs: Revolutionizing Potato Disease Detection Through Machine Learning
Abstract: Advancements in agricultural technology and the integration of artificial intelligence for diagnosing plant and leaf diseases are crucial for sustainable agricultural development. Conditions like early blight and late blight exert a notable influence on both the quality and quantity of potato harvests. Identifying these leaf diseases manually demands significant labor and a considerable level of expertise. Therefore, efficient, and automated methods for disease detection are essential to improve potato production. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 54–62 Read article
-
Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites
Abstract: Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Crop Yield Prediction Using Machine Learning Algorithm Based on Climate Variables
Abstract: India's economy is based primarily on agriculture, as over 50% of the country's population depends on it for their livelihood. The long-term viability of agriculture is seriously threatened by variations in the weather, climate, and other environmental factors. Because machine learning provides tools for decision assistance in agricultural yield prediction, including guidance on which crops to plant and when to plant them during the growing season, it is essential to …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 49–52 Read article
-
An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article