Prevention of cocoa moniliasis using Progressive Web Applications and sensor data in the province of Francisco de Orellana

Abstract

Ecuador is an essential cocoa producer recognized for its quality and aroma. Additionally, it holds a prominent position among the country's traditional export products, making it the third-largest cocoa-producing country in the world. However, the cocoa industry faces challenges due to moniliasis, a fungal disease that affects cocoa trees and causes damage to the fruits, resulting in decreased production. This research aims to prevent cocoa moniliasis by conducting tests with different algorithms to select the best one for predicting moniliasis using sensor data in the progressive web application. Various supervised learning algorithms were applied, including PCA, IPCA, KPCA, Linear Regression, Sci-Kit Learning, and ensemble methods like Bagging and Boosting. Google's Lighthouse is utilized for artifact validation. It is concluded that the Boosting ensemble method with a value of 1.0 and 4 estimators is the algorithm that shows a good fit for prediction. In artifact validation, it yields favorable results with a score of over 90 in various Lighthouse parameters. Keywords: Moniliasis 1; Progressive Web Application 2; PCA 3; IPCA 4; KPCA 5; Linear Regression 6; Bagging 7; Boosting 8; Lighthouse 9