Influence of Xception and DenseNet121 Architectures for Plant Disease Detection

Abstract

This paper proposes the maximum achievable solution to the diagnosis of leaf diseases by employing deep learning and image enhancement tools.

A hybrid model has been developed, with the architectures using Xception and DenseNet121, to classify leaves into four groups as healthy, diseased, rusty, and scab. The model is trained using many different datasets of plant leaf images. The result is enhanced using data augmentation techniques to improve the robustness and generalization of the model. We program and adopt a method to use the Xception and DenseNet121 models. The combination model was trained under a 10-cycle training program, which is broken down into development, maintenance, and perturbation.

Other criteria to assess our method's performance include accuracy, loss, and validation scores. The results will depict the possibility of our method for early plant diseases detection that can impact agriculture through the timely intervention process. Overall accuracy achieved is 93.70%

Our research contributes to the exploration since the use of techniques in crop management and the control of diseases may increase yields and reduce the use of pesticides, thus the extension of precision agriculture.