Fresh Fruit Bunch Ripeness Classification Methods: A Review

Abstract

AbstractThe escalating demand for palm oil necessitates enhanced production strategies. As the trend shifts towards automated harvesting to meet the demand, precise ripeness classification has become pivotal. Manual methods are inefficient and error-prone because of workforce constraints. The present review scrutinizes the following non-destructive ripeness classification methods: spectroscopy, inductive sensing, thermal imaging, light detection and ranging, laser-light backscattering imaging, and computer vision. The review focuses on identifying reliable techniques capable of real-time and accurate classification in dynamic and unstructured environments. All aforementioned techniques are discussed in intricate detail, accompanied by thorough critiques. This review then presents a performance comparison and benchmarking process, providing comprehensive insights into the strengths and weaknesses of each technique. A compelling solution emerges in the fusion of light detection and ranging and computer vision techniques. This synergy capitalizes on their strengths to offset individual limitations, offering a potent approach. Furthermore, this fusion yields added value in terms of localization and mapping, rendering it exceptionally suitable for real-time classification in complex environments. This review provides insights into bridging the gap between automated harvesting needs and ripeness assessment precision, thereby fostering advancements in the palm oil industry.