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

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Abstract

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.

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Authors and Affiliations

  1. Department of Information Technology, Assosa University, Assosa, Ethiopia

    Wasyihun Sema Admass

  2. Department of Information Technology, Injibara University, Injibara, Ethiopia

    Yirga Yayeh Munaye

  3. Department of Computer Science, Assosa University, Assosa, Ethiopia

    Girmaw Andualem Bogale

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  1. Wasyihun Sema Admass
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  2. Yirga Yayeh Munaye
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  3. Girmaw Andualem Bogale
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Correspondence to Wasyihun Sema Admass.

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Admass, W.S., Munaye, Y.Y. & Bogale, G.A. Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification. Int. j. inf. tecnol. 16, 817–829 (2024). https://doi.org/10.1007/s41870-023-01605-z

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