Hybrid Decision Support System Framework for Leaf Image Analysis to Improve Crop Productivity

Abstract

Crop disease is one of the major problems with agriculture in India. Identifying the disease and classifying the type of disease is most important which can be made possible using the deep learning technique. To perform this verified dataset is required which consists of healthy and disease leaf images of all crops. The proposed model uses a hybrid approach which integrates VGG16 classifier with an attention mechanism, transfer learning approach and dropout operation. The proposed model uses a rice disease dataset and us i ng the proposed approach it achieves an train accuracy of 96.45 percent and train loss 0.09 and validation loss of 0.44. The dataset is collected from the plant village project for rice leaf which consists of 4955 images which include Brown Spot, Healthy, Hipsa, and Leaf Blast type of images. The proposed model use attention mechanism that focuses mainly on the part of the image rather than the whole part of the image using a glimpse ratio of 3:1. The traditional method of detecting crop diseases needs high experience and knowledge of experts in the field which is time consuming, ineffective, and high cost. In this study, Deep Convolutional Neural Networks (DCNN) and Transfer Learning with Attention models are used to detect diseases associated with rice plants without overfitting the model.

Augmented Rice Plant Disease Detection with Convolutional Neural Networks

“…Research [11] uses the Deep Convolutional Neural Network (DCNN) method to detect rice disease types with precision 0.962, recall 0, 0.9617, specificity 0.9921, and F1-score 0.9616. Research [12] uses the CNN method to detect rice disease types as much as 4955 data with an accuracy of 96%. Research [13] utilizes deep learning technology for apple fruit type detection of as many as 1990 images with an accuracy of 98%.…”