Object Detection Algorithms for Ripeness Classification of Oil Palm Fresh Fruit Bunch

Abstract

Ripe oil palm fresh fruit bunch allows extraction of high-quality crude palm oil and kernel palm oil. As the fruit ripens, its surface color changes from black (unripe) or dark purple (unripe) to dark red (ripe). Thus, the surface color of the oil palm fresh fruit bunches may generally be used to indicate the maturity stage. Harvesting is commonly done by relying on human graders to harvest the bunches according to color and number of loose fruits on the ground. Non-destructive methods such as image processing and computer vision, including object detection algorithms have been proposed for the ripeness classification process. In this paper, several object detection algorithms were investigated to classify the ripeness of oil palm fresh fruit bunch. MobileNetV2 SSD, EfficientDet (Lite0, Lite1 and Lite2) and YOLOv5 (YOLOv5n, YOLOv5s and YOLOv5m) were simulated and compared in terms of their mean average precision, recall, precision and training time. The models were trained on a dataset with four main ripeness classes: ripe, unripe, half-ripe, and over-ripe. In conclusion, object detection algorithms can be used to classify different ripeness levels of oil palm fresh fruit bunch, and among the different models, YOLOv5m showed promising results with a mean average precision of 0.842 (0.5:0.95).

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

“…Disparities observed among previous studies indicate that classification accuracy is contingent upon several factors, including the size of training, testing, and validation datasets. For instance, studies employing small training datasets (comprising fewer than 1000 images) yield moderate to high classification accuracy [17][18][19][20]. Additionally, the choice of CNN algorithm significantly influences classification accuracy, with certain algorithms, such as YoLo, exhibiting notably high accuracy rates [18][19][20], albeit primarily designed for detection rather than classification tasks.…”

“…A comprehensive review of the related literature and research reveals the utilization of machine learning techniques for processing images to determine the ripeness levels of harvested oil palm fruits, categorized into multiple levels to aid in sorting and assessing the quality of factory purchases for setting purchase prices [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Notably, the evaluation of classification accuracy demonstrates the high precision achieved through machine learning, particularly with the application of deep learning algorithms [18][19][20][21][22][23][24][25][26]. However, despite these advancements, the analysis underscores several limitations in existing research.…”

“…However, despite these advancements, the analysis underscores several limitations in existing research. Notably, the absence of an application or platform capable of real-time data processing [17][18][19][20][21][22][23][24][25][26], reliance on a limited number of datasets for model creation leading to potential overfitting issues in practical scenarios [17,[21][22][23]28,29], and the lack of clarity regarding the number of datasets used for modeling and testing [24,25]. Furthermore, the digital images employed for classification predominantly feature harvested oil palm fruit with intact backgrounds, rendering them unsuitable for on-tree oil palm fruit classification [17][18][19][20][21][22][23][24][25][26].…”

“…Notably, the absence of an application or platform capable of real-time data processing [17][18][19][20][21][22][23][24][25][26], reliance on a limited number of datasets for model creation leading to potential overfitting issues in practical scenarios [17,[21][22][23]28,29], and the lack of clarity regarding the number of datasets used for modeling and testing [24,25]. Furthermore, the digital images employed for classification predominantly feature harvested oil palm fruit with intact backgrounds, rendering them unsuitable for on-tree oil palm fruit classification [17][18][19][20][21][22][23][24][25][26]. While some research endeavors have proposed the development of real-time monitoring applications for oil palm fruit ripeness [30], these initiatives are not without limitations, as the proposed models remain static and unmodifiable, potentially impacting their accuracy during real-world applications.…”

Optimizing viscosity measurement: an automated solution with YOLOv3

“…You Only Look Once (YOLO) is a high-speed object detection model which quickly generates object location and classification. YOLOv3 is a version of the YOLO model that works well for detecting small objects using multi-scale prediction [13][14][15]. This is necessary because the ball object to be detected appear small when capture by the camera.…”