Integrating Local Texture Capturing Mechanisms With Convolutional Neural Networks For Enhanced Multi‑Class Classification of Plant Leaf Diseases

Abstract

Plant diseases significantly affect agricultural productivity by reducing both the quality and quantity of crops. The necessity for automated image‑based solutions stems from the labor‑intensive and subjectively error‑prone nature of traditional inspection methods performed by farmers or agricultural specialists. To maintain sustainable agriculture and prevent the spread of infections, the detection of plant leaf diseases should be performed early and accurately. Early identification of infections can also significantly reduce yield losses and minimize the excessive use of pesticides. Since leaf diseases frequently manifest as uneven texture patterns, spots, or distortions on the leaf surface, local texture capturing mechanisms have proven to be remarkably effective among many computational approaches. This study proposes a novel Deep Convolutional Neural Network (DCNN) to extract high‑level hidden feature representations from leaf images. To enhance performance, the deep features are combined with traditional handcrafted texture features known as the Uniform Local Binary Pattern (uLBP). The proposed model was trained and tested using three well‑known publicly available datasets: Apple Leaf, Tomato Leaf, and Grape Leaf. The model achieved test accuracies of 96%, 91%, and 96% on these datasets, respectively. The experimental results demonstrate that the proposed approach is an effective and practical method for early diagnosis of plant diseases. This system has potential for real‑world application by farmers and agricultural experts to support disease management and contribute to the development of more resilient crops and a sustainable agricultural industry.

Plant phenotyping relevance

植物葉画像から病害状態を推定する画像解析手法を提案・評価しており、病害表現型の取得・分類が研究の中心であるため。

Code and data availability

The paper uses publicly available PlantVillage-derived Apple, Tomato, and Grape leaf datasets, but these are cited prior-work datasets (ref [12]) rather than paper-specific deposits. No author analysis code, trained models, or paper-specific data repository is provided; the Data Availability Statement only promises a '

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