Journal of Advanced Research in Agriculture Science and Technology
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech
Advanced Research Publicationsen-USJournal of Advanced Research in Agriculture Science and TechnologyAnalyzing the Impact of IoT in Agriculture: Data-Driven Insights into Crop Production, Waste Reduction, and Profitability
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech/article/view/1544
<p>The advent of the Internet of Things (IoT) in agriculture has fundamentally transformed traditional farming practices, introducing groundbreaking solutions that enhance crop production, minimize waste, and maximize profitability. By integrating advanced technologies such as smart sensors, automated systems, and data analytics, IoT has redefined efficiency <br />and sustainability in farming. This paper presents a comprehensive evaluation of IoT’s tangible impact on agriculture, utilizing quantitative data, graphical representations, and detailed case studies. The findings reveal notable improvements in crop yields, resource optimization, and economic outcomes, underscoring IoT’s pivotal role as a transformative <br />force in the agricultural sector.</p>Nirbindu BorAjay Gupta
Copyright (c) 2025 Journal of Advanced Research in Agriculture Science and Technology
2025-06-282025-06-28811922Leveraging Machine Learning for Enhanced Agricultural Productivity: A Crop Recommendation System Approach
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech/article/view/1547
<p>Agriculture is fundamental to global food security, but challenges such as climate change, soil degradation, and fluctuating market prices hinder productivity. The integration of Machine Learning (ML) into agricultural practices has shown promising results in improving crop yield predictions and resource optimization. This paper explores a novel machine learning-based crop recommendation system that tailors crop choices based on various environmental, economic, and agricultural factors. By utilizing ML algorithms, this system can offer precise recommendations that help farmers make informed decisions, ultimately increasing agricultural productivity and sustainability. The proposed method promises to revolutionize crop selection, enabling better utilization of available resources and enhancing food security in a changing climate.</p>Suraj R BindMadhav Jha
Copyright (c) 2025 Journal of Advanced Research in Agriculture Science and Technology
2025-06-302025-06-308112Combating Crop Diseases in India: The Role of IoT and AI in Early Detection and Prevention
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech/article/view/1548
<p>Crop diseases remain a significant challenge in Indian agriculture, leading to substantial economic losses and reduced food security. Traditional methods of disease detection and management often fail due to late identification and lack of resources, especially in rural regions. This paper explores the role of Internet of Things (IoT) and Artificial Intelligence (AI) in combating crop diseases by enabling early detection, prediction, and prevention. By integrating these technologies, farmers can reduce losses, improve crop yield, and promote sustainable agricultural practices. The paper reviews existing applications, benefits, and challenges of implementing these technologies in India, offering a path towards a more resilient agricultural future.</p>Gaurav Avadhesh MishraAnanya Sunil Kshatriya
Copyright (c) 2025 Journal of Advanced Research in Agriculture Science and Technology
2025-06-302025-06-308138Image Processing for the Identification of Leaf Disease
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech/article/view/1549
<p>This paper explores the application of image processing techniques for the detection and classification of plant leaf diseases, which is crucial for maintaining agricultural productivity. The study focuses on corn, a significant crop economically, and addresses the challenges posed by various corn leaf diseases. Advanced image processing methodologies, such as image acquisition, preprocessing, segmentation, and feature extraction, are utilized to enhance disease identification accuracy. Techniques like color transformation, noise removal, k-means clustering, and Otsu’s thresholding are employed to segment and analyze affected leaf areas.</p>Rahul PalRishabh Singh
Copyright (c) 2025 Journal of Advanced Research in Agriculture Science and Technology
2025-06-302025-06-3081913Leveraging Solar Energy and IoT for Smart Irrigation: A Sustainable Solution for Indian Agriculture
https://medicaljournalshouse.com/index.php/Journal-AgricultureSciTech/article/view/1550
<p>The agricultural sector in India is facing significant challenges, including inefficient irrigation practices, water scarcity, and increasing energy costs. Solar-powered IoT-based irrigation systems offer a potential solution to address these challenges. By utilizing solar energy to power irrigation pumps and leveraging IoT technologies to automate irrigation, water usage can be optimized, and energy consumption reduced. This research explores the feasibility of solar-powered smart irrigation systems for Indian agriculture, alongside proposing a digital platform to educate farmers and facilitate access to essential technologies. The paper reviews the current state of irrigation in India, highlights the benefits of solar-powered IoT systems, and identifies the challenges to their widespread adoption. Additionally, the research proposes a digital platform to bridge the gap between farmers’ needs and access to technology, thereby enhancing productivity and promoting sustainable farming practices.</p>Rahulkumar Vijaykumar Gupta Vikash Ram Narayan Gupta
Copyright (c) 2025 Journal of Advanced Research in Agriculture Science and Technology
2025-06-302025-06-30811418