Image-Based Plant Disease Classification for the Management of Crop Health

Categorizing plant diseases is crucial for ensuring agricultural production and food security. In this research, we investigate two distinct methods for classifying plant diseases: Convolutional neural networks (CNN) for deep learning and logistic regression (LR) along with Random Forest Classifier (RFC) for machine learning. We use a collection of plant pictures representing various diseases to train and evaluate LR and CNN models. The CNN model automatically learns hierarchical representations, while the LR model relies on manually created features extracted from the images. Our analysis reveals that both LR and CNN models achieve high accuracy in classifying plant diseases, with CNN surpassing LR due to its ability to recognize complex image patterns. The CNN model's performance in our experiment outperforms other models in terms of accuracy. The experiment's findings underscore the effectiveness of deep learning and machine learning techniques in classifying plant diseases.