CFO-RetinaNet: Convolutional Feature Optimization for Oil Palm Ripeness Assessment in Precision Agriculture

Alfariz, Muhammad Alkam and Santoso, Hadi (2025) CFO-RetinaNet: Convolutional Feature Optimization for Oil Palm Ripeness Assessment in Precision Agriculture. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 11 (2). pp. 251-262.

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Abstract

Accurate ripeness assessment of oil palm fruit bunches (FFB) is critical for optimizing yield and quality in the palm oil industry, yet manual grading remains subjective and labor-intensive. This study proposes CFO-RetinaNet, an enhanced RetinaNet framework integrating deformable convolutions and hybrid attention mechanisms to optimize multi-scale convolutional features for robust ripeness classification under variable field conditions. Our key contribution is threefold: (1) a novel dataset of 4,728 high-resolution, expert-annotated FFB images spanning five ripeness stages (Immature to Decayed), collected under diverse lighting and occlusion scenarios in Central Kalimantan, Indonesia; (2) a feature optimization pipeline combining adaptive feature fusion and dynamic focal loss to improve discriminative capability for nuanced inter-class distinctions; and (3) a scalable deep learning solution validated through rigorous field testing. The model achieves a mean average precision (mAP) of 83.6% and an F1-score of 98.3%, outperforming YOLOv5 (82.5% mAP) and Faster R-CNN (76.4% mAP), with 18.5% fewer misclassifications than standard RetinaNet. It retains 99% accuracy in low-light conditions and reduces labor costs by automating error-prone grading tasks. By publicly releasing the dataset and framework, this work advances precision agriculture standards, offering a transferable solution for ordinal maturity classification in perennial crops while supporting sustainable palm oil production through optimized harvesting decisions.

Item Type: Artikel Umum
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi: Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User: M.Eng. Alfian Ma'arif
Date Deposited: 08 Jul 2025 06:37
Last Modified: 08 Jul 2025 06:37
URI: http://eprints.uad.ac.id/id/eprint/84763

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