Classifying the Ripeness of Oil Palm Fresh Fruit Bunches Using a Mixed-Order Relation-Aware Recurrent Neural Network Approach for Enhanced Agricultural Monitoring and Optimized Crop Management in Agriculture

Abstract

Oil palm production has increased rapidly since 2012, particularly in Guatemala, Malaysia, and Indonesia. Accurate Fresh Fruit Bunch classification is vital for oil yield and quality, but manual grading is inefficient and existing deep learning methods are computationally intensive. To overcome these challenges, this study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed-Order Relation-Aware Recurrent Neural Network (OP-FFBR-MORA-RNN). The proposed methodology integrates Confidence Partitioning Sampling Filtering (CPSF) for effective image localization, cropping, and resizing, followed by Revised Tunable Q-Factor Wavelet Transform (RTQFWT) to extract discriminative features. These features are subsequently classified using a Mixed-Order Relation-Aware Recurrent Neural Network (MORA-RNN), with its parameters optimized via the Fractional Pelican African Vulture Optimization (FPAVO) algorithm to enhance classification accuracy. The model categorizes FFBs into five ripeness classes: overripe, ripe, abnormal, empty fruit, and under-ripe. Experimental evaluation on an oil palm dataset demonstrates that OP-FFBR-MORA-RNN achieves 97.8% accuracy, 97.6% precision, and a low error rate of 2.4%, outperforming existing methods such as Oil Palm Fresh Fruit Bunch Ripeness categorization on mobile devices using Convolutional Neural Network (OP-FFBR-MD-CNN), Machine Vision for maturity classification using Artificial Neural Network (MVM-OP-FFBR-ANN), and Object Detection for Oil Palm Fruit Bunches using You-Only-Look-Once (OB-OPFBR-YOLOv7). These results confirm the framework's effectiveness for reliable, scalable, and efficient ripeness classification, supporting improved agricultural monitoring and optimized crop management.