Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection

Abstract

Plant diseases lead to a significant decrease in product efficiency and economic losses for producers. However, early detection of plant diseases plays an important role in preventing these losses. Today, Convolutional Neural Network (CNN) models are widely used for image processing in many fields such as face recognition, climate, health, and agriculture. But in these models, the weights of the layers are randomly initialized during training, which increases training time and decreases performance. With the method known as Transfer Learning in the literature, CNN models are trained on large databases such as ImageNet. Then, pretrained CNN models are created using the weights obtained in this training. Thus, training time decreases while performance improves. In this study, standard and pretrained versions of popular CNN models DarkNet-19, GoogleNet, Inception-v3, Resnet-18, and ShuffleNet have been used for automatic classification of diseases from leaf images of potato, cotton, bean, and banana. In the experimental study, the classification performances of all these standard and pretrained CNN models are presented comparatively. Experimental results have shown that the performance of CNN models is significantly improved by transfer learning, even in a small number of epochs.

Mobile APP for Banana Leaf Spot Disease Detection and Classification using Convolution Neural Network Model

“…The accuracy based on texture feature for class healthy, yellow sigatoka, and panama are 82.3%, 70.7%, and 63.5%. SonerKiziloluk [8] used several deep learning models such as DarkNet, GoogleNet, Inception, Resnet and ShuffleNet to classify disease of potato, banana, cotton, and bean plants. The applied standard CNN models increased the accuracy from 7% to 25% with the transfer learning method of 5 epochs.…”