Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease

Artificial Intelligence has been proving a great boon in almost all the sector of industries. In recent times the demand for food has increased, whereas the supply still lacks. In order to meet these increasing demands, prevention and early detection of crop disease are some of the measures that must be inculcated in farming to save the plants at an early stage and thereby reducing the overall food loss. In this paper, we use a deep learning-based model and transfer learning-based models to classifying images of diseased plant leaves into 38 categories of plant disease based on its defect on a Plant Village dataset. Eight pre-trained models namely VGG16, VGG19, ResNet50, InceptionV3, InceptionResnetV2, MobileNet, MobileNetV2, DenseNet along with the one self-made model were used in our study. We found that DenseNet achieves the best result on the test data with an accuracy of 99%.