IoT Innovations in Cotton Plant Disease Detection for Sustainable Agriculture

Authors

  • Jayashree Katti Professor, IT Department, Pimpri Chinchwad College of Engineering, Pune- 411044
  • Gulbakshee Dharmale Assistant Professor, IT Department, Pimpri Chinchwad College of Engineering, Pune- 411044
  • Swapnaja Amol Ubale Associate Professor Information Technology Department, Marathwada Mitra Mandal College of Engineering, Pune
  • Radha Deoghare Assistant Professor, Pimpri Chinchwad College of Engineering, Nigdi
  • Shiv Havgirao Sutar School of Computer Engineering and Technology, MIT World Peace University,Pune

Keywords:

Internet of Things (IoT), Cotton Plant Diseases, Precision Agriculture, Sensor Networks, Unmanned Aerial Vehicles (UAVs), Machine Learning, Sustainable Agriculture, Early Detection, Environmental Monitoring, Precision Farming

Abstract

Cotton, a crucial cash crop in the textile industry, faces significant threats from various diseases that can impact both yield and fiber quality. This research explores the integration of Internet of Things (IoT) innovations to revolutionize cotton plant disease detection, providing real-time monitoring and data-driven decision support for sustainable agriculture practices. The proposed system employs wireless sensor networks deployed in cotton fields, UAVs equipped with advanced imaging technology, and a centralized data processing platform. These components collect crucial environmental parameters, such as temperature, humidity, and soil moisture, alongside high-resolution images of the cotton crops. The dataset is then transmitted to a centralized platform where machine learning algorithms and analytics are applied for precise disease detection. Machine learning models, trained on diverse datasets containing images of cotton plants with various diseases, analyze incoming data to identify potential outbreaks promptly. Upon detection, the system triggers automated responses, such as notifying farmers or activating precision-targeted treatment protocols, minimizing environmental impact and optimizing resource usage. Implementation of this IoT-driven disease detection system not only enables early intervention but also contributes to sustainable agriculture by reducing reliance on broad-spectrum pesticides and optimizing yield. The collected data supports long-term trend analysis, offering insights into crop management practices and encouraging the adoption of precision agriculture. This research demonstrates the efficacy of IoT technologies in addressing critical challenges in cotton farming, providing a scalable and adaptable solution for sustainable agriculture. By presenting a comprehensive framework for disease detection and management, this study aims to contribute to the ongoing discourse on utilizing technological innovations for ensuring food and fiber security in an ever-changing agricultural landscape.

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Published

07.02.2024

How to Cite

Katti, J. ., Dharmale, G. ., Ubale, S. A. ., Deoghare, R. ., & Sutar, S. H. . (2024). IoT Innovations in Cotton Plant Disease Detection for Sustainable Agriculture. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 651–658. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4827

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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