AI-Driven Plant Disease Prediction Using Deep Learning and Web-Based Deployment

Abstract: Plant diseases seriously affect agricultural productivity and cause economic loss and food insecurity. Herein, this paper presents an AI-based plant disease prediction system that uses Convolutional Neural Networks for image- based diagnosis. The model was trained based on the PlantVillage dataset containing over 80,000 labeled images from 38 classes of diseases. The CNN model was able to classify crop leaf diseases with more than 95% accuracy. In addition, the system provides a Streamlit-based web interface that facilitates real-time disease prediction by enabling users to upload leaf images and get instant diagnostic feedback. Experimental results showed that the deployed system is scalable, low latency, and of high precision, and hence can be used for practical early disease detection and smart agriculture. Future enhancements include mobile deployment, analysis using real-time cameras, and integration with IoT-enabled sensors for precision farming.

Keywords: Plant Disease Detection, Convolutional Neural Network (CNN), Deep Learning, Streamlit, Image Classification, Smart Agriculture, Computer Vision, AI in Farming