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%.