k-Nearest Neighbor and Feature Extraction on Detection of Pest and Diseases of Cocoa

Pusadan, Mohammad Yazdi; Syahrullah,; Merry,; Abdullah, Ahmad Imam

doi:10.29207/resti.v6i3.4064

Cited by 4 publications

(3 citation statements)

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“…However, the study used a simple algorithm, the certainty factor, which may not be able to detect all pests and diseases. Mohammad Yazdi et al [12] have proposed a model for building a system to detect pest and disease types in cocoa pods. This study uses digital image processing techniques to extract color characteristics from digital images of cocoa pods.…”

Section: Hue Saturation Value (Hsv)mentioning

confidence: 99%

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

MAMADOU,

AYIKPA,

BALLO

et al. 2023

IJACSA

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Cocoa cultivation is of immense importance to the people of Côte d'Ivoire. However, this culture is experiencing significant challenges due to diseases spread by various agents such as bacteria, viruses, and fungi, which cause considerable economic losses. Currently, the methods available to detect these cocoa diseases force farmers to seek the expertise of agronomists for visual inspections and diagnostics, a laborious and complex process. In the search for solutions, many studies have opted for using convolutional neural networks (CNNs) to identify diseases in cocoa pods. However, an essential advance is to develop hybrid approaches that combine the advantages of a CNN with sophisticated classification algorithms. This research stands out for its innovative contribution, combining MobileNetV2, a convolutional neural network architecture, with algorithms, such as Logistic Regression (LR), K Nearest Neighbors (KNN), Support Vector Machines (SVM), XGBoost, and Random Forest. The study was conducted in two distinct phases. First, each algorithm was evaluated individually, and then performance was measured when MobileNetV2 was merged with the algorithms mentioned. These hybrid approaches complement and amplify MobileNetV2's capabilities. To do so, they draw on MobileNetV2's inherent capabilities to extract key features and enhance information quality. By combining this expertise with the classification methods of these other models, hybrid approaches outperform individual techniques. Accuracy rates range from 72.4% to 86.04%.This performance amplitude underlines the effectiveness of the synergy between the extraction characteristics of MobileNetV2 and the classification skills of other algorithms.

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Section: Hue Saturation Value (Hsv)mentioning

confidence: 99%

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

MAMADOU,

AYIKPA,

BALLO

et al. 2023

IJACSA

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“…Digital image processing techniques are used to simplify and speed up the process of testing the maturity level of oil palm fruit which will then be classified using a classification method, namely the K-Nearest Neighbor (KNN) algorithm [8]- [10]. K -Nearest Neighbor (KNN) is a method for classifying objects or data based on learning data taken from k nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm

Mohammad Yazdi Pusadan,

Indah Safitri,

Wirdayanti

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The oil palm is one of the monocot oil-producing plants in Indonesia. Sorting errors in oil palm fruit are caused by a sorter error when distinguishing the color of ripe and immature oil palm fruit. In addition to inefficient time, the area of oil palm plantations is also a factor that causes the sorter to make mistakes in sorting. This study aims to produce a system that can classify the maturity of oil palms based on feature extraction of characteristics of the hue, saturation and value (HSV) color features. The HSV method is used to produce color characteristics from the image of the oil palm fruit. Classification of oil palm fruit maturity is classified using the K-Nearest Neighbor (KNN) algorithm with a dataset of 400 oil palm fruit image data with a data sharing ratio of 70% training data and 30% test data. 280 image data were used as training data, divided into 140 image data of ripe oil palm fruit, 140 image data of immature oil palm fruit and 120 image data of oil palm used as test data which is divided into 60 image data of ripe oil palm and 45 unripe palm oil. Based on the result of tests that have been carried out using a confusion matrix with varied k values, namely, 5 and 7, the average precision is 94.16%.

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“…So here it encourages the author to make an idea to design a system that becomes the background to be presented in this study with the title "Classification of betel nut maturity based on HSV images with KNN". With this research, it is hoped that in addition to helping betel nut farmers, there is also a classification of areca nut sold to the public so that the quality of the betel nut received by the community is better based on the level of maturity (Yang, et al, 2021;Thirani, et al, 2022;Pusadan & Abdullah, 2022).…”

Section: Introductionmentioning

confidence: 99%

Classification of Maturity Levels in Areca Fruit Based on HSV Image Using the KNN Method

Sarimole¹,

Rosiana²

2022

JAETS

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Areca nut (Areca catechu) is a kind of palm plant that grows in Asia and Africa, the eastern part of the Pacific and in Indonesia itself, areca nut can also be found on the islands of Java, Sumatra and Kalimantan. At the stage of classifying the maturity of the betel nut so far, it is still using the manual method which at that stage has subjective weaknesses. Based on these problems, researchers will create a system that is able to classify the level of maturity of areca nut using HSV feature extraction with assistance at the classification stage using the KNN method. In this study, 842 datasets were used which were divided into 3 types of classes, namely ripe, unripe and old fruit. The dataset was divided into 683 training data and 159 test data. In the next stage, the data is tested using the K-Nearest Neighbor method by calculating the closest distance using k = 1. From the results of the calculation of the closest distance k1 produces an accuracy rate of 87.42%. Kata kunci— Matlab, Areca Ripeness, KNN, HSV.

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k-Nearest Neighbor and Feature Extraction on Detection of Pest and Diseases of Cocoa

Cited by 4 publications

References 0 publications

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms

The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm

Classification of Maturity Levels in Areca Fruit Based on HSV Image Using the KNN Method

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