Article: Automatic leaf diseases detection and classification of cucumber leaves using internet of things and machine learning models Journal: International Journal of Web and Grid Services (IJWGS) 2023 Vol.19 No.3 pp.350 - 388 Abstract: Automation of agriculture with the use of cutting-edge technology is a growing research area. It addresses the issue of better yields and tries to mitigate the negative impact due to climatic changes, attacks of diseases, and pests in crops. Hence to overcome the problem of disease attacks, this research proposes an automatic leaf disease detection and classification system using a web app that can help the farmer to identify the occurrence of leaf diseases remotely. Further performance matrices like confusion matrix, overall classification accuracy, precision, sensitivity, specificity, and ROC-AUC score have been calculated to test the efficacy of models. The simulated results proved that the CNN with 10-fold cross-validation has got an accuracy of 99.47% and it significantly outperforms other existing counterparts. The data collected from both environments have been compared and analysed. This study offers a real-time application of the internet of things and machine learning in agriculture. Inderscience Publishers - linking academia, business and industry through research

Title: Automatic leaf diseases detection and classification of cucumber leaves using internet of things and machine learning models

Authors: Swana Prabha Jena; Sujata Chakravarty; Siba Prasad Sahoo; Shubham Nayak; Subrat Kumar Pradhan; Bijay Kumar Paikaray

Addresses: Department of ECE, Centurion University of Technology and Management, Odisha, India ' Department of CSE, Centurion University of Technology and Management, Odisha, India ' Department of CSE, Centurion University of Technology and Management, Odisha, India ' Department of CSE, Centurion University of Technology and Management, Odisha, India ' Department of ECE, Centurion University of Technology and Management, Odisha, India ' School of Information and Communication Technology, Medhavi Skills University, Sikkim, India

Abstract: Automation of agriculture with the use of cutting-edge technology is a growing research area. It addresses the issue of better yields and tries to mitigate the negative impact due to climatic changes, attacks of diseases, and pests in crops. Hence to overcome the problem of disease attacks, this research proposes an automatic leaf disease detection and classification system using a web app that can help the farmer to identify the occurrence of leaf diseases remotely. Further performance matrices like confusion matrix, overall classification accuracy, precision, sensitivity, specificity, and ROC-AUC score have been calculated to test the efficacy of models. The simulated results proved that the CNN with 10-fold cross-validation has got an accuracy of 99.47% and it significantly outperforms other existing counterparts. The data collected from both environments have been compared and analysed. This study offers a real-time application of the internet of things and machine learning in agriculture.

Keywords: cucumber leaf; leaf diseases; internet of things; IoT sensors; machine learning; classification; pre-trained models.

DOI: 10.1504/IJWGS.2023.133506

International Journal of Web and Grid Services, 2023 Vol.19 No.3, pp.350 - 388

Received: 11 Apr 2023
Accepted: 10 Jun 2023
Published online: 18 Sep 2023 *

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Title: Automatic leaf diseases detection and classification of cucumber leaves using internet of things and machine learning models

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