Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm

Abstract

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.