Advancing Plant Disease Detection: A Comparative Analysis of Deep Learning and Hybrid Machine Learning Models

Abstract

Detecting plant diseases is essential for precision agriculture, as it enhances crop production and ensures the security of the food supply. We adopted two methods for this research: a method based on deep learning, through Convolutional Neural Networks (CNN), and a hybrid model using classical machine learning. The dataset comprised images of plant leaves from Kirtan village in Hisar, Haryana, which were annotated by plant pathologists. The CNN model, which autonomously extracts hierarchical spatial features, achieved an accuracy of 97.57%, making it ideal for large datasets. Conversely, the Hybrid model utilizing handcrafted GLCM and LBP features and SVM classifiers achieved 91.73% accuracy while providing interpretability and computational efficiency in resource limited setups. The performance of the models was measured in terms of accuracy, precision, recall and F1-score. Applications range from on-line monitoring with drones to diagnostic equipment for the farmer.