Title: Potato late blight disease detection using convolutional neural network

Authors: Mominul Islam; Md. Ashraful Islam; Ahsan Habib

Addresses: Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Kumargaon, Sylhet-3114, Bangladesh ' Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Kumargaon, Sylhet-3114, Bangladesh ' Institute of Information and Communication Technology, Shahjalal University of Science and Technology, Kumargaon, Sylhet-3114, Bangladesh

Abstract: This paper proposes a convolutional neural network-based deep learning model to classify and detect the infectious potato leaves suffering from late blight disease. The proposed model has two classifiers - the potato leaf classifier and the late blight disease classifier. Both healthy and diseased plant leaf images were taken from the plant. Village dataset and real-time images are used to train, validate and test the classifiers. A total of 4,680 and 1,470 plant leaf images are used for the two classifiers, respectively. The potato leaf classification accuracy of the proposed model is 97.12%. The proposed CNN model also provides an accuracy of 98.62% while identifying late blight disease. The ten-fold cross-validation technique is used to observe the performance of the proposed late blight classifier and then compared with other cutting-edge approaches. In observation, it has been shown that the proposed technique outperformed many other existing techniques.

Keywords: late blight; convolutional neural network; deep learning; image processing; image augmentation.

DOI: 10.1504/IJICT.2023.134828

International Journal of Information and Communication Technology, 2023 Vol.23 No.4, pp.346 - 370

Received: 10 Jul 2021
Accepted: 09 Oct 2021
Published online: 14 Nov 2023 *

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