Detection of Citrus Plant Leaf Detection Using Non – Imaging Data

Abstract

Plant leaf diseases pose a significant challenge to agricultural productivity, necessitating efficient and accurate detection methods. This study presents an integrated approach combining deep learning (CNN) and machine learning (SVM, Random Forest, KNN, Logistic Regression) for plant disease classification using imaging and spectral data. The proposed system processes leaf images to detect unhealthy regions, extracting statistical features such as contrast (10.59), entropy (4.31), and mean intensity (137.24) to assess disease severity. CNN-based models demonstrated strong training accuracy but exhibited overfitting in validation performance. Among machine learning models, SVM and Logistic Regression achieved the highest accuracy (70%), while Random Forest performed moderately (54%), and KNN struggled (39%) due to high-dimensional spectral complexities. Confusion matrices revealed that Healthy and Greening categories often overlapped, leading to misclassifications. The findings suggest that a hybrid deep learning + machine learning approach enhances classification accuracy by leveraging both image-based and spectral features. Future improvements involve ensemble learning, better feature engineering, and real-time field deployment for automated disease detection. This research provides a scalable and effective solution for precision agriculture, enabling early disease diagnosis and improved crop health monitoring.

Plant phenotyping relevance

画像およびスペクトルデータから葉の病徴・重症度を推定する分類手法を開発・評価しており、植物表現型の取得が中心的な貢献である。

Code and data availability

The paper describes a citrus disease dataset (FieldSpec 4 ASD spectral data and RGB leaf images) and CNN/ML analysis, but provides no public repository, deposit, or availability statement for the dataset, images, code, or models. The dataset table even notes metadata 'Needs improvement' and pending size analysis, with

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