Physics-Informed Neural Network-Assisted Imaging for Oil Palm Fruit Ripeness Classification

Abstract

In this work, we present a Physics-Informed Neural Network (PINN) framework for the classification of oil palm fresh fruit bunch (FFB) ripeness using RGB images. Unlike conventional Convolutional Neural Networks (CNNs) that learn solely from visual patterns, the proposed PINN integrates a physics-based index—derived from the red-to-green pixel intensity ratio—directly into the network architecture and loss function. This hybrid design embeds wavelength-dependent physical knowledge related to chlorophyll degradation during ripening, enabling the model to learn more robust and generalizable features even with limited and imbalanced training data. The PINN model achieves a peak accuracy of 0.73, outperforming the purely data-driven CNN baseline (0.68) by a margin of 5%. Overall, the PINN demonstrates superior performance in minority-class detection and maintains stable convergence under three different lighting conditions (different light spectra). These results highlight the effectiveness of integrating domain-specific physical insights into deep learning models, offering a promising pathway toward reliable, non-destructive, and automated ripeness assessment for agricultural applications.

Plant phenotyping relevance

RGB画像から油ヤシ果房の成熟度という植物器官の状態を推定するPINN手法を開発し、CNNとの比較および異なる照明条件で性能評価しており、表現型取得・推定が中心である。

Code and data availability

The paper's 112 oil palm FFB RGB images and trained CNN/PINN models are explicitly declared proprietary and unavailable; no public dataset, code, or model repository is provided.

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