Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique

Abstract

High quality palm oil is critical in ensuring Malaysia’s competitiveness in the sector. Studies have shown that there is a significant relationship between the quality of palm oil produced and the ripeness of the fruits used in producing the oil. Correct ripeness of the fresh fruit bunches (FFB) produces higher quality and more oil content. Unripe FFB produces the least oil and overripe FFB produces oil of lower quality. According to Malaysian Palm Oil Board (MPOB), the main factors that determine the ripeness of the oil palm FFB are its colour and the number of its loose fruits. To classify the ripeness according to these two factors, there are 3 common techniques has been implemented in the previous work which are; colour feature extraction, texture feature extraction and Deep Learning method. To handle fruit ripeness classification problem, this paper provides a short review to the reader to grasp the applicable technique that can be implemented.

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

“…Although studies have highlighted CNN's superior classification accuracy [26], many prior investigations utilizing machine learning algorithms like KNN, SVM, and ANN have achieved remarkably high accuracy rates in classifying oil palm fruits or bunch ripeness, ranging between 97% and 100% [27][28][29][30]. Regarding the application of deep learning in classifying and detecting the ripeness of bunches or oil palm fruits, the analysis reveals a classification accuracy stratified into the following three levels: very accurate (accuracy exceeding 95%) [24,25], highly accurate (accuracy ranging between 80% and 94%), and moderately accurate (accuracy between 60% and 79%) [22,23], with the majority of outcomes falling within the highly accurate range. Disparities observed among previous studies indicate that classification accuracy is contingent upon several factors, including the size of training, testing, and validation datasets.…”

“…However, limitations arise from the absence of support for classifying photographs of oil palm bunches on trees or unharvested crops in most previous studies and presentations. This deficiency stems from the exclusive utilization of datasets comprising videos or images of harvested oil palm bunches [17][18][19][20][21][22][23][24][25][26][27][28][29]. Furthermore, certain studies do not facilitate realtime image processing using smartphones or mobile devices [20,24], and the limited size of datasets employed for model creation and testing adversely impacts the reliability and accuracy of the model [20][21][22][23][27][28][29].…”

“…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.…”