Oil Palm Fresh Fruit Bunch Detection and Ripeness Classification Using YOLOv5

Mohamed Yasser Mohamed Ahmed Mansour Katrina D. Dambul Kan Yeep Choo 

Vol.106 No.10 pp.886-891

Publication Date:2023/10/01

Online ISSN:2188-2355

Print ISSN:0913-5693

Type of Manuscript:Special Section on Artificial Intelligence of Things (AIoT) for Smart Farming


Oil palm fresh fruit bunchesripeness classificationobject detectionYOLO

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In oil palm estates, smart farming technology such as visual-based mobile applications can improve the harvesting process by assisting in the oil palm fresh fruit bunch (FFB) ripeness classification process and allow the monitoring of the harvesting process remotely. In this paper, oil palm FFB detection and ripeness classification is developed using a YOLOv5s model. Augmentation and label smoothing techniques were applied to improve the performance of the model. The results after applying label smoothing techniques showed that the mean average precision of the YOLOv5s model is 87.85%(0.5:0.95) with a precision of 96.19% and a recall of 95.19%. The results after image augmentation (Mosaic and Cut-Out) showed that the mean average precision of the YOLOv5s model is 86.67%(0.5:0.95) with a precision of 95.78% and a recall of 98.00%. This model can potentially be implemented in a mobile device to be used in a real time harvesting process.



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Online ISSN:2188-2355