Plant Disease Detection- CNN-based Framework for Automated Plant Disease Identification and Healthcare Monitoring

Abstract

This research introduces an innovative deep learning framework utilizing convolutional neural networks (CNNs) for the automated identification of plant pathologies. The proposed system employs TensorFlow and Keras libraries to develop a sophisticated classification model capable of distinguishing between healthy and diseased plant specimens through image analysis. The neural network architecture incorporates essential components, including convolutional layers for feature extraction, pooling layers for spatial dimension reduction, flattening operations for data transformation, and fully-connected dense layers for final classification decisions. Through comprehensive experimentation and optimization, the developed model achieved an exceptional classification accuracy of 98.33% on test datasets. This automated diagnostic system offers significant practical value for agricultural stakeholders by enabling early disease detection, facilitating timely intervention strategies, and contributing to enhanced crop protection measures that can substantially reduce agricultural losses and improve overall farming productivity.