Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Abstract

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

“…By obtaining the ratio of pixels corresponding to a specific color to classify the different ripening stages of strawberries. Similar conclusions were obtained in related studies [20].…”

“…Azarmdel et al [19] segmented mulberry images using RGB color space and selected the B channel as the best channel to classify the fruit into three categories (unripe, ripe, and overripe). Alfatni et al [20] used multivariate techniques to extract fruit image features and combine the information for oil palm species classifying and ripeness testing. By combining a chromatic aberration map of citrus fruits under normal conditions with a luminance map under light, Lu et al [21] effectively solved the problem of the effect of light on the identification of citrus ripeness by using color features for threshold segmentation.…”

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.…”

Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism

“…Advancements in computer technology have given rise to traditional image-processing techniques, including image pre-processing, feature extraction, dimensionality reduction, and recognition. These methods have been applied to agricultural images for various purposes, for example, classifying crop pest and disease locations and types [6][7][8]. Nan Xu [9] utilized image processing and traditional machine recognition techniques for crop pest detection and analyzed their effectiveness.…”