A Hybrid Model for Crop Disease Detection Based on Deep Learning and Support Vector Machine

Pakistan's agriculture sector is the backbone of its economy, contributing significantly to its gross domestic product (GDP). However, a key challenge in this sector is to counteract the crop diseases timely because these diseases result in reduced production, increased cost and eventually lead to economic loss. Traditional disease control methods are costly, time-consuming, and often lack technical support, resulting in poor disease management and harmful environmental consequences. This research harnesses the unmatched capability of Artificial Intelligence (AI) and deep learning for timely disease detection in crops. This research introduces a hybrid model that combines deep learning models with a machine learning classifier for disease detection. AlexNet, Vgg-16, ResNet50, and MobileNet are the deep learning models that have been employed for the detection of various diseases in crop leaves of rice, potato, and corn. These models have been trained by using healthy and diseased leaf images of the mentioned crops and then these models are combined with a Support Vector Machine (SVM) classifier to enhance the accuracy of detection. Experimental results show the outstanding performance of this hybrid approach for timely disease detection in crops. It is further observed that the combination of Mobile Net and SVM results in an impressive accuracy of 95.68% in disease detection. This technological approach would be beneficial for farmers in the effective management and control of crop diseases thus improving the crop yield and ultimately contributing to economic growth.