Classification of Oil Palm Plant Diseases Based on Hybrid Deep Learning Using U-Net and ResNet-

Abstract

Oil palm (Elaeis guineensis) productivity was frequently constrained by foliar diseases, which were often difficult to detect at an early stage using conventional visual inspection methods. To address this challenge, the present study proposed a hybrid deep learning framework for automated oil palm leaf disease detection. A dataset comprising 1,200 oil palm leaf images, equally distributed across three disease classes (400 images per class), was utilized. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets, with training and validation data obtained from public repositories, while testing data were collected directly to ensure model generalizability. The proposed hybrid architecture combined U-Net for precise leaf lesion segmentation, ResNet-50 as a deep feature extractor to capture high-level discriminative representations. U-Net segmentation enabled isolation of infected regions, while ResNet-50 provided robust feature embeddings that enhanced separability between visually similar disease classes. Experimental evaluation demonstrated that the baseline U-Net + SVM approach achieved an accuracy of 84.2%, precision of 82.5%, recall of 83.1%, and F1-score of 82.8%. In contrast, the hybrid U-Net + ResNet-50 + SVM method yielded superior results with 91.6% accuracy, 90.8% precision, 91.2% recall, and 91.0% F1-score, reflecting an improvement of approximately 7.4%.

Plant phenotyping relevance

油ヤシ葉の病変領域を画像から分割・分類する深層学習法の開発と性能評価が研究の中心であり、植物の病害状態を直接推定しているため。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.