Automatic Detection and Classification of Mango Disease Using Convolutional Neural Network and Histogram Oriented Gradients

Abstract

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 accuracy of the proposed model is 98.80%. 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.

Plant phenotyping relevance

マンゴー葉・植物の病徴を画像から検出・分類するCNN/HOG手法が研究の中心であり、植物病害状態の表現型推定に該当する。

Code and data availability

The paper describes a CNN-HOG mango disease detection model using locally collected images and a Kaggle-sourced dataset, but provides no public deposit, availability statement, or authors' URL for the dataset, code, or trained models. The Kaggle reference lacks a concrete public URL, and all allowed_urls are cited-prio

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