Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm
Cacao has been one of the most promising crops produced in the Philippines due to its increasing demand in various local and international markets. Although cacao production aspired to be heightened to cope with the global trend, several difficulties were still needed to be addressed in cro...
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| Language: | English |
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Institute of Technology and Education Galileo da Amazônia
2025-01-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1367 |
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| author | Earl Clarence San Diego Seph Gerald Rodrin Edwin Arboleda |
| author_facet | Earl Clarence San Diego Seph Gerald Rodrin Edwin Arboleda |
| author_sort | Earl Clarence San Diego |
| collection | DOAJ |
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Cacao has been one of the most promising crops produced in the Philippines due to its increasing demand in various local and international markets. Although cacao production aspired to be heightened to cope with the global trend, several difficulties were still needed to be addressed in crop propagation, mainly due to disruptive diseases and pests. In response to this problem, the study devised an algorithm based on k-Nearest Neighbors that can detect whenever a cacao pod was infected with the three most prominent diseases: black pod rot, Monilia, and pod borer infestations. The machine training model was preceded with visual feature extraction of color and texture parameters representing the cacao pod samples. It was found that the fine k-Nearest Neighbors algorithm achieved the highest validation and testing accuracies of 93.44% and 96.67%, respectively. The study's outcome suggested the continuous practicality of fusing visual feature extraction processes with supervised machine learning to generate models that can be applied to improve agricultural methods.
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| format | Article |
| id | doaj-art-009562b9d6004fef93cf5cb06bc28483 |
| institution | Kabale University |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-009562b9d6004fef93cf5cb06bc284832025-02-06T23:51:52ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-01-01115110.5935/jetia.v11i51.1367Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors AlgorithmEarl Clarence San Diego0Seph Gerald Rodrin1Edwin Arboleda2Cavite State UniversityDepartment of Computer, Electronics and Electrical Engineering, Cavite State University, Don Severino Delas Alas Campus, Indang, Cavite, PhilippinesDepartment of Computer, Electronics and Electrical Engineering, Cavite State University, Don Severino Delas Alas Campus, Indang, Cavite, Philippines Cacao has been one of the most promising crops produced in the Philippines due to its increasing demand in various local and international markets. Although cacao production aspired to be heightened to cope with the global trend, several difficulties were still needed to be addressed in crop propagation, mainly due to disruptive diseases and pests. In response to this problem, the study devised an algorithm based on k-Nearest Neighbors that can detect whenever a cacao pod was infected with the three most prominent diseases: black pod rot, Monilia, and pod borer infestations. The machine training model was preceded with visual feature extraction of color and texture parameters representing the cacao pod samples. It was found that the fine k-Nearest Neighbors algorithm achieved the highest validation and testing accuracies of 93.44% and 96.67%, respectively. The study's outcome suggested the continuous practicality of fusing visual feature extraction processes with supervised machine learning to generate models that can be applied to improve agricultural methods. http://itegam-jetia.org/journal/index.php/jetia/article/view/1367 |
| spellingShingle | Earl Clarence San Diego Seph Gerald Rodrin Edwin Arboleda Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm ITEGAM-JETIA |
| title | Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm |
| title_full | Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm |
| title_fullStr | Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm |
| title_full_unstemmed | Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm |
| title_short | Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm |
| title_sort | classification of prominent cacao pod diseases using multi feature visual analysis and k nearest neighbors algorithm |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1367 |
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