FROM ONTOLOGY TO INFERENCE: A COMPUTATIONAL PHILOSOPHICAL FRAMEWORK OF IoT-ML FOR READING THE MATURITY AND VOLUME OF PALM OIL

Abstract

The timing of harvesting in oil palm plantations necessitates objective and rapid ripeness assessment, coupled with an estimation of extractable oil volume. This paper presents a philosophical-computational framework with an end-to-end architecture integrating Internet of Things (IoT) sensors and machine learning (ML) for the classification of fresh fruit bunch (FFB) ripeness levels and oil volume regression. The approach rests on explicit ontological and epistemological foundations, operationalizes latent targets through standardized field protocols, and implements reproducible ML practices. We delineate a multimodal pipeline (RGB imagery + environmental sensors + weight), a late fusion modeling strategy (CNN embeddings + tabular features), and an evaluation design that emphasizes cross-block generalization, model explainability, and drift monitoring. Performance targets include an F1-macro ≥ 0.88 for ripeness classification and a Mean Absolute Error (MAE) ≤ 4 ml/kg for oil volume regression on out-of-block data. Discussions also encompass the ethics and axiology of transparency, data governance, and economic impacts, along with future directions such as federated learning and portable hyperspectral integration.