AI-Based Plant Disease Recognition System: A CNN Approach with Dual Deployment via Web and Telegram

Author(s)	Mr. Nayan Deepak Rita, Mr. Aahil Rafique Noori, Netra Anil Rajde, Archana Chaugule
Country	India
Abstract	Early detection of plant diseases is crucial for improving crop yield and reducing economic losses. This paper presents a CNN-based plant disease recognition system using a hybrid dataset of over 87,000 images across 38 classes. An EfficientNet-based transfer learning model is employed to achieve high accuracy while maintaining computational efficiency. The system incorporates advanced features such as context-aware analysis using environmental data, economic loss estimation, and explainable AI through Grad-CAM. To ensure accessibility, it is deployed via a Streamlit web application and a Telegram chatbot, along with a multilingual voice interface. Experimental results show improved performance, achieving 96–97% accuracy, making the system suitable for real-world agricultural applications.
Keywords	Plant disease detection, convolutional neural network, transfer learning, EfficientNet, Streamlit, Telegram bot, PlantVillage, precision agriculture, Grad-CAM, multilingual voice assistant
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-20
DOI	https://doi.org/10.63363/aijfr.2026.v07i03.5039

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