NL-FuRBe: Precision Diagnosis of Citrus Leaf Diseases using Image Enhancement and Non-Linear Fuzzy Ranking Ensemble Approach

Abstract

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 Non-Linear 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 f iltering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning architectures—VGG19, AlexNet, and Xception—using a fuzzy rank-based scoring mechanism built on non-linear 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 avearge 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

柑橘葉の症状を画像から検出・分類する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。

Code and data availability

The paper's core phenotyping input is a public lemon leaf disease image dataset (1354 images, 9 classes) deposited on Mendeley Data, explicitly cited as the training/evaluation dataset and named in the Data Availability Statement with DOI and URL. No author analysis code or trained model checkpoints are disclosed.