Precision diagnosis of citrus leaf diseases using image enhancement and nonlinear fuzzy ranking ensemble approach NLFuRBe.

Abstract

Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques. The study emphasizes the significance of timely disease diagnosis in citrus crops, which are vital for global food security and economic stability. The methodology begins with image quality enhancement through Vector-Valued Anisotropic Diffusion (VAD) and morphological filtering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning (DL) architectures-VGG19, AlexNet, and Xception-using a fuzzy rank-based scoring mechanism built on nonlinear transformations (exponential, tanh, and sigmoid functions) to address prediction uncertainty and model bias. A comprehensive dataset of lemon leaf diseases, consisting of 1354 images across nine classes, was utilized for training and evaluation. Experimental results using five-fold cross-validation demonstrate that the proposed model achieves superior performance with an average accuracy of 96.51%, outperforming conventional ensemble and state-of-the-art approaches. The results validate the proposed NL-FuRBE as an effective, automated, and cost-efficient tool for precision agriculture and early disease diagnosis in citrus farming.

Plant phenotyping relevance

柑橘葉の病徴を画像から検出・分類する手法を新規開発し、画像処理、アンサンブル分類、交差検証で技術性能を評価しているため、植物病害状態の表現型取得が中心である。

Code and data availability

The paper's lemon leaf disease image dataset (1354 images, nine classes) is publicly deposited on Mendeley Data with explicit availability statement and DOI; no author code or models are shared.