Oil Palm Fresh Fruit Bunch Ripeness Detection Methods: A Systematic Review

Abstract

The increasing severity of the labour shortage problem in the Malaysian palm oil industry has created a need to explore other avenues for harvesting oil palm fresh fruit bunches (FFBs) such as through autonomous robots’ deployment. However, the first step in using an autonomous system to harvest FFBs is to identify which FFBs have become ripe and are ready to be harvested. In this work, we reviewed previous and current methods of identifying the maturity of fresh fruit bunches as found in the literature. The different methods were then compared in terms of the types of sample data used, sensor modalities, and types of classifiers used with a particular focus on the feasibility of each method for on-field application. From the 51 papers reviewed, which include a total of 11 unique approaches, it was found that the most feasible method for detecting ripe FFBs in the field is a combination of computer vision and deep learning. This system has the advantages of being a noncontact approach that is low cost while also being able to operate in real time with high accuracy.

Bioenergetic Cultures: Estimate of Oil Content in Macaw Palm via Computer Vision

“…Instantaneous and non-destructive methods for assessing maturity stages and quantifying oil content in palm fruits have been investigated (Alfatni et al, 2022;Septiarini et al, 2021;Lai et al, 2023) to develop techniques that can assist in decision-making based on the harvest time and selection of fruits, thus reducing the necessity for laboratory analysis.…”

“…In this context, computer vision systems have been applied as an alternative for evaluating, selecting, and monitoring various processes involving agricultural products (Bhargava & Bansal, 2021;Amani et al, 2022). In palm trees, digital images have been successfully applied to evaluate the maturation stages (Ali et al, 2020;Lai et al, 2023), perform post-harvest monitoring (Septiarini et al, 2021) and estimate the oil content (Matsimbe et al, 2015;Oliveira et al, 2021). This technique, which involves multivariate analysis and machine learning, enables the development of protocols and technologies that can reduce sampling costs, allows an instantaneous evaluation of samples without the necessity to destroy the fruit, and facilitates the determination of standardization parameters for fruit classification and selection (Khan et al, 2021).…”

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

“…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.…”