Title: Plant leaf disease classification using deep neural network

Authors: N. Kasthuri; T. Meera Devi; Arivazhagan T. Shangar; R. Yashwin; J.S. Shabhareesh

Addresses: Kongu Engineering College, Perundurai, Erode, Tamilnadu, India ' Kongu Engineering College, Perundurai, Erode, Tamilnadu, India ' Kongu Engineering College, Perundurai, Erode, Tamilnadu, India ' Kongu Engineering College, Perundurai, Erode, Tamilnadu, India ' Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

Abstract: Agriculture is the backbone of Indian economy. Most of the people living in rural areas depend on agriculture for their livelihood. Nevertheless, the farmers are facing a lot of difficulties in crop production due to climatic change. In addition, diseases in plants affect the production of crops drastically. Presently, the farmers identify the plant diseases by visual inspection which, in turn, requires an expert's help and it is a time consuming task. Hence, in this paper, deep learning networks are used to identify different types of diseases in the leaves of different plants. The model is trained with 45,562 images and validated with 8,049 images belonging to 17 categories of diseases. It is fine-tuned with the hyperparameters such as learning rate, epochs, batch size and input image size and then tested with 9,469 images which yield a total classification accuracy of 96.8%.

Keywords: multiclass classification; convolutional neural network; CNN model; model parameters; activation function; support vector machine; SVM classifier; transfer learning; plant leaf diseases; hyperparameters; performance metric.

DOI: 10.1504/IJCVR.2022.125353

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.5, pp.443 - 463

Received: 27 May 2021
Accepted: 18 Jul 2021
Published online: 07 Sep 2022 *

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