Hydra: An ensemble deep learning recognition model for plant diseases

Abstract

Indian agriculture contributes about 17% to the total GDP and employs over 60% of the population. Due to the subjectiveness and time-consuming nature of manual approaches to disease detection, large or small-scale farming faces many challenges. Cotton diseases such as Areolate, Mildew, Sore Shin, Fusarium, Wilt, and Myrothecium hugely affect crop yield. The Hydra framework-based ensemble deep learning model proposes to conduct symptom-wise recognition for cotton disease. Hydra Framework is an ensemble method of Convolution Neural Network (CNN) and VGG16 model with SoftMax function and ReLU (Rectified Linear Unit) that improves the performance of CNN by removing the negative values obtained from each layer of feature extraction. A real-time field study from Thadikombu village, Dindigul District, Tamil Nadu, is considered. The data augmentation technique is used to overcome the overfitting issue, thereby enlarging the available dataset. With 15,600 images containing healthy and diseased leaves, the ensemble Hydra model achieved an accuracy of 95% for recognizing diseases and was validated with pictures collected from Thadikombu village, Dindigul District, Tamilnadu. A comparison is made with the results obtained from the proposed Ensemble Hydra model with CNN and fine-tuned VGG model. Results showed Ensemble Hydra model was a helpful tool for recognizing cotton diseases.