Cotton Plant Disease Detection and Classification Using Cloud  Computing

Author(s)	Ganapathi S R, Ajay R, Gopinath S, Sumathi G
Country	India
Abstract	Our methodology leverages transfer learning using pre-trained deep learning models such as VGG-16, VGG-19, Inception, and Xception V3. High-resolution thermal images are captured via Raspberry Pi-equipped sensors and transmitted via the lightweight MQTT protocol to a cloud platform. These images are analyzed in the cloud, and the system identifies disease categories, provides diagnostic reasoning, and suggests mitigation strategies. The proposed solution achieves a classification accuracy of up to 98%, making it an efficient and scalable alternative for precision farming applications.
Keywords	cotton plant disease detection, deep learning, cloud computing, transfer learning, vgg-16, vgg-19, inception, xception v3, mqtt, raspberry pi, iot.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-10
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6011
Short DOI	https://doi.org/g9pz6t

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International Journal on Science and Technology