A novel plant disease prediction model based on thermal images using modified deep convolutional neural network

With the advancement of deep learning and thermal imaging technology, prediction of plant disease before the appearance of any visual symptoms gains attention. Studies showed that before the appearance of any visual symptoms, some internal changes take place in the plant that cannot be detected externally. These changes may be captured by the thermal images which will help to predict the diseases at the earlier stage. This early prediction will increase the probability and time to recovery; reduce the use of pesticide, resulting in cost effective, quantitative and qualitative production with less environmental pollution. In this study a plant disease prediction system based on thermal images has been developed by exploring the dynamic feature extraction capability of deep learning technology. The proposed system consists of three convolutional layers to overcome the computational overhead and the over fitting problem for small dataset. The system has been tested with a very common disease Bacterial Leaf Blight, of rice plants. The proposed model has been evaluated using several metrics like accuracy, precision, type-I error, type-II error. This novel model predicts the disease at the earliest stage (within 48 h of the inoculation) with 95% accuracy and high precision 97.5% (2.3% Type-I error and 7.7% Type-II error). A comparative study has been done with four standard deep learning models -VGG-16, VGG-19, Resnet50 and Resnet101 and also with machine learning algorithms -Linear Regression and Support Vector Machine, to establish the superiority of the proposed model.