弗朗西斯科-德奥雷亚纳省利用渐进式网络应用程序和传感器数据预防可可单胞菌病

Darwin Romero, Pilar Oña, Pedro Aguilar, Wilson Chango
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引用次数: 0

摘要

厄瓜多尔是一个重要的可可生产国,其可可的质量和香味广受认可。此外,可可在该国的传统出口产品中占有重要地位,使其成为世界第三大可可生产国。然而,可可产业面临着单胞菌病带来的挑战。单胞菌病是一种影响可可树的真菌疾病,会对可可果实造成损害,从而导致产量下降。本研究旨在通过对不同算法进行测试,选择最佳算法,利用渐进式网络应用程序中的传感器数据预测可可单孢菌病,从而预防可可单孢菌病。应用了各种监督学习算法,包括 PCA、IPCA、KPCA、线性回归、Sci-Kit 学习以及 Bagging 和 Boosting 等集合方法。谷歌的 Lighthouse 用于人工验证。结果表明,Boosting 集合方法的值为 1.0,有 4 个估计器,是一种非常适合预测的算法。在人工验证中,该算法取得了良好的结果,在 Lighthouse 的各种参数中得分超过 90 分。关键词Moniliasis 1; Progressive Web Application 2; PCA 3; IPCA 4; KPCA 5; Linear Regression 6; Bagging 7; Boosting 8; Lighthouse 9
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prevention of cocoa moniliasis using Progressive Web Applications and sensor data in the province of Francisco de Orellana
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
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