Federated Learning for Grading Oil Palm Fruit Ripeness in the Oil Palm Industry

The prevailing method for grading oil palm fruit bunches in mills relies on human graders conducting visual inspections, resulting in frequent errors and inconsistent assessments. This is a significant open problem when developing a detector for oil palm fruit ripeness and oil content, which are related factors. Current trends focus on computer vision techniques based on image processing and machine learning to improve grading of oil palm fruit bunches at an individual factory, resulting in limited diversity of the data used for evaluation. Collecting data from all factories offers informational advantages but raises privacy concerns. Addressing these challenges, this study proposes federated learning (FL) to develop local and global prediction models for grading oil palm fruit ripeness while preserving data privacy. FL facilitates collaborative model training across factories, mitigating privacy risks and enhancing model development efficiency. The proposed model uses the color of palm husks to determine the ripeness stage, which is used as a factor in predicting the yield of oil from the crop. A predictive model was created using FL principles with a training dataset of 5209 images, which was divided into two subsets: single‐palm (2571 images) and multipalm (2638 images). The classification accuracy of a global model was 90.0%, while the local models were expanded to include private data for each of 4 testing clients. The predictive global and local models from FL were used to implement the system in a web application form and to validate its performance in calling the oil palm ripeness stage.