LGENetB4CA: A novel deep learning approach for chili germplasm Differentiation and leaf disease classification

Abstract

The primary objective of this study was to evaluate the reliability of chili leaf image classification as a method for distinguishing chili germplasm accessions, thereby supporting germplasm conservation efforts. Traditional methods for identifying chili varieties rely on manual leaf observation, which is labor-intensive and error-prone, highlighting the need for advanced fine-grained classification techniques. To address this challenge, we introduce a curated chili leaf database, JNUCLS, to support future research. We evaluate state-of-the-art deep learning methods for chili variety classification and propose a novel approach, LGENetB4CA, which combines a modified EfficientNetB4 model with a LeafGabor filter. The EfficientNetB4 incorporates a Coordinate Attention block following its layers to effectively capture both spatial and channel-wise information. This enables the model to focus on critical regions, such as fine-grained leaf textures, while maintaining a global context. The LeafGabor filter enhances intricate leaf details, such as vein structures, while suppressing noisesuch as blurred shadows, significantly improving input quality. Experiments on the JNUCLS and COLD chili datasets demonstrate high accuracy in chili variety and leaf disease classification, with LGENetB4CA achieving 89.61% accuracy on JNUCLS and 85.90% on COLD chili. These findings highlight the potential of leaf image classification as an effective, cost-efficient tool for exploring phenotypic diversity among chili cultivars. The proposed method also demonstrates promise for broader applications, including plant classification systems, targeted crop management, agricultural product tracking, market analysis, and biodiversity preservation.

Plant phenotyping relevance

葉画像から品種差と葉病害を分類する深層学習手法を開発・評価し、データセットも構築しているため、植物表現型取得・分類が中心的です。

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