ConvNet of Deep Learning in Plant Disease Detection

Automation in the agriculture field is a priority when compared with other fields, as the latest growth of Agriculture and Farming is dependent on technologies for production. The next major important requirement in Plant diseases is early prediction and necessary related recommendations. In this research, the proposed method is implemented with the plant leaf dataset, which consists of diseased and healthy data of various plant leaves. The prediction and classification of the diseased plant leaves is achieved by deploying the deep neural network models ResNet50, AlexNet and Proposed model ProliferateNet. Finally, the experimental output values of these models show the significance of the Neural Network models in the detection of plant disease, as well as the efficiency of neural networks. During training a Neural Network model, data augmentation can solve a number of issues, including limited or imbalanced data, overfitting, variance, and complexity. The dataset is augmented using image-based data augmentation techniques before being applied to deep neural network. The accuracy of the various models is evaluated, and ProliferateNet attained an average training accuracy of 93% and testing accuracy of 99%.