Classification of Soybean Leaf Disease from Environment effect Using Fine Tuning Transfer Learning

Abstract

Deep learning Algorithms which involve training a Convolutional Neural Networks, have shownmajordevelopment in solving image classification, object detection, forecasting, segmentation problems and many computer vision problems. Soybean crop is widely cultivated in the world, thus identifying Soybean leaves and its disease can be an important work in deep learning and agriculture area. We have taken soybean dataset for training our deep learning model. The dataset contains 321 images under eight different classes of soybean leaf diseases. The dataset contains small amount of images which is a challenging task to train Deep CNN from scratch. Aiming this problem, transfer learning is used in addition to increasing the dataset size with the help of data augmentation methods. It is observed that dataaugmentation operations generalize the pre-trained CNN models and also increase the classification accuracy. We have compared the performance of six pre-trained deep CNNs with the traditional machine learning. The performance of this work is compared using Accuracy, Precision, Recall, Specificity, F1 score and MCC with the assistance of confusion matrix so as to seek out the best-suited model.Experimental results show that the finetuned ResNet50 network has achieved maximum accuracy among Alexnet, Resnet18, VGG16, VGG19 and GoogleNet i.e. 93% which is better than previous methods.