Transforming Agriculture with IoT and Deep Learning: A Smart Approach to Precision Farming and Sustainability

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ID: 312727
2025
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Abstract
There is tremendous challenges in the agricultural sector, like climate change, depletion of resources and rapidly rising demand for food across the world. Advanced technologies are required in traditional farming methods that, if not efficient and sustainable, demand such integration. A combination of Internet of Things (IoT) and Deep Learning (DL) is a transformative solution to modern agriculture problems. In this paper, three vital areas where this technology could, which are precision irrigation, crop disease detection, and resources optimization, are explored employing deep learning and IoT. Smart farming can use Long Short-Term Memory (LSTM) networks for time series predictions and Convolutional Neural Networks (CNNs) for disease diagnosis from imaging to increase efficiency, reduce resource wastage, and increase crop yield. With real-time sensor data combined with predictive analytics farmers have data driven decision making power, enabling them to reduce water consumption, pesticide overuse and operational costs. While data integration, high implementation costs and connectivity constraints hinder IoT adoption, development in IoT infrastructure and AI models make the scalability and accessibility more bearable. The contributions of this paper are to highlight the potential of IoT and deep learning to lead to a more productive, sustainable, intelligent agricultural ecosystem.
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imported_1777056300_69ebba2c78184 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ali Raza
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/855
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