Model for Monitoring the Pest Carmenta Foraseminis in Cocoa Crops Through Environmental Parameters

Abstract

Cocoa consumption has increased in recent years as it has a significant impact on the food and cosmetic sector. It is therefore important to maintain and improve agricultural productivity, which makes it urgent and necessary to improve management techniques, including fertilization and crop and plantation protection practices. However, intensive cocoa production in Peru and South America faces challenges for cocoa production. The most decisive is the attack of the “mazorquera” pest (Carmenta foraseminis), which infests the fruit inside without giving the possibility of early detection and, consequently, the quality and intensity of the harvest is often diminished. The objective of this work is the development and implementation of a method to monitor and detect the “mazorquera” pest in the cocoa crop, by obtaining data in real time and analyzing them. In this sense, the proposal involves the use of sensors to monitor certain environmental variables that correlate with the behavior of the pest “mazorquera” from certain artificial intelligence algorithms. The use of intelligent algorithms decreases the analysis time, the error rate and increases the accuracy in the decision-making process. The proposed method proves to have the relevant correlation between environmental conditions and the behavior with crop pests, allowing its timely detection to intervene quickly and effectively. The proposed procedure is not limited to the improvement in crop management, it also creates the conditions for improved traceability of information, improving cocoa production, making it more competitive and sustainable.