Oil palm fruit ripeness detection using K-Nearest neighbour

Abstract

Palm oil plant (Elaeis guineensis jacq) is one of the most popular plants in Indonesian plantation and also a species of the palm family. Harvesting process should obtain fresh fruit bunches at optimal ripeness. Some farmers are lack of ripeness knowledge and do not understand which color that represents optimal ripeness of palm fruit to harvest. Another issue on the scarcity of such system provides ripeness identification also encourage a study to develop an image-processing-based expert system. The aim of this study is to provide a tool for examining oil palm fruit ripeness level. Identification system consists of several image processing phases, namely image edge detection and feature value of the image to be calculated such as mean value, standard deviation, skewness, entropy, energy, homogeneity, contrast and intensity. In the proposed work, K-Nearest Neighbor (KNN) is applied as a computation method which allows the system to classify data of ripeness level based on user-entered image. The output is the identification of the ripeness level of the oil palm fruit.

Video based oil palm ripeness detection model using deep learning

“…The key advancement produced in this research is in the form of a video dataset with six classes of oil palm maturity levels, which is more suitable to real-world settings than non-sequential image datasets. This research investigated multi-category video data because the current research has not tested multi-category data, therefore the findings of the existing research are unsatisfactory, and most of the previous research employs a classification model [ 2 , 3 , 5 , 6 ] that cannot recognize several objects in one picture frame. Even so, using video datasets necessitates a large amount of data in order for model detection to perform well.…”

Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

“…Since machine learning can derive laws from sample data that can hardly be summarized by theoretical analysis, many researchers have conducted extensive and in-depth research on techniques for object detection and recognition of fruits and vegetables based on the K-means clustering algorithm [68][69][70][71][72][73][74][75], SVM algorithm [54,57,69,73,[76][77][78][79][80][81][82][83][84], KNN clustering algorithm [36,[85][86][87][88][89][90][91], AdaBoost algorithm [62,[92][93][94][95][96][97][98][99], decision tree algorithm [100][101][102][103][104][105][106][107], and Bayesian algorithm [108]…”

“…Techniques for object detection and recognition of fruits and vegetables based on the KNN clustering algorithm are more widely used. Based on the KNN clustering algorithm, Tan et al [36], Astuti et al [90], Suban et al [89], Sarimole and Rosiana [85], and Sarimole and Fadillah [86] detected and recognized the ripeness of blueberries, oil palms, papayas, betel nuts, and pomegranates, respectively.…”

“…(2) Image segmentation and classifiers based on traditional machine learning, such as K-means clustering algorithm-based methods [68][69][70][71][72][73][74][75], SVM (Support Vector Machine) algorithm-based methods [54,57,69,73,[76][77][78][79][80][81][82][83][84], KNN (K Nearest Neighbor) clustering algorithm-based methods [36,[85][86][87][88][89][90][91], AdaBoost (Adaptive Boosting) algorithm-based methods [62,[92][93][94][95][96][97][98][99], decision tree algorithm-based methods [100][101][102][103][104][105][106][107], and Bayesian algorithmbased methods [108]…”

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

“…Past researchers have developed automated grading systems which use image processing method to evaluate the red, blue, and green (RBG) elements from the fruit bunch images. Classification methods such as k-Nearest Neighbor (KNN) were also used to compare feature values in each image according to the smallest differences from each study data [ 9 ]. However, due to the changes in light intensity throughout the day, images captured at different times may cause some discrepancies when determining the ripeness of oil palm fruits using the computer vision technique [ 10 ].…”