Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification

作     者：Admass, Wasyihun Sema Munaye, Yirga Yayeh Bogale, Girmaw Andualem 

作者机构：Department of Information Technology Assosa University Assosa Ethiopia Department of Information Technology Injibara University Injibara Ethiopia Department of Computer Science Assosa University Assosa Ethiopia 

出 版 物：《International Journal of Information Technology (Singapore)》 (Int. J. Inf. Technol.)

年 卷 期：2024年第16卷第2期

页      面：817-829页

主　　题：Automatic detection Classification Convolutional neural network Histogram oriented gradient Hybrid approach Mango disease 

摘      要：This study suggests a convolutional neural network (CNN) and histogram oriented gradients (HOG)-based automatic detection and classification system for mango disease. Early detection is essential for efficient disease management since mango disease can have a major influence on fruit quality and yield. The suggested system makes use of the CNN algorithm for extracting features and the HOG technique for capturing shape and texture data. The extracted features are subsequently used to feed a disease classification model for disease detection. The efficiency of the proposed model is demonstrated by experimental findings, which achieve excellent accuracy in both disease detection and classification tasks. The CNN-HOG hybrid model outperforms CNN or HOG alone in terms of performance, demonstrating the complementary nature of these two methods for the detection and classification of mango disease. The system s performance is evaluated using measures for accuracy, precision, and recall and the proposed model accuracy achieved a training accuracy of 98.80% and a testing accuracy of 99.5%. This research helps establish effective and trustworthy tools for managing mango disease by automating the detection and classification process. This enables prompt intervention and reduces crop losses. © The Author(s), under exclusive licence to Bharati Vidyapeeth s Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.