Early Identification and Classification of High-Impact Cotton Plant Diseases through IoT
DOI:
https://doi.org/10.32628/IJSRST251314Keywords:
Cotton Plant Diseases, IoT in Agriculture, Convolutional Neural Networks (CNN), Smart Farming, Disease Detection, Image-Based Classification, Precision Agriculture, Bacterial Blight, Cotton Leaf Curl Virus (CLCuV), Fusarium Wilt, Real-Time Monitoring, Sensor-Based Diagnosis, Marathwada Cotton Farming, Environmental Sensing, Deep Learning in Agriculture, Agricultural Automation, Flask Web Application, Arduino-Based System, DHT11 Sensor Soil Moisture MonitoringAbstract
Cotton, often referred to as “white gold,” is one of India's most critical cash crops, forming the backbone of both the agricultural and textile sectors. However, despite its economic significance, cotton cultivation is increasingly threatened by the emergence of severe plant diseases such as Bacterial Blight, Cotton Leaf Curl Virus (CLCuV), and Fusarium Wilt. These diseases not only diminish yield but also exacerbate rural distress, especially in drought-prone regions like Marathwada, Maharashtra. This research explores the biological characteristics and economic impact of these diseases, reviews conventional and advanced detection methods, and proposes a hybrid technological framework involving IoT sensors and Convolutional Neural Networks (CNN) for early disease diagnosis. With real-time monitoring, image-based classification, and predictive analytics, this model aims to empower farmers, reduce production losses, and promote sustainable cotton farming practices.
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