外文摘要：The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F1-score of 0.88, with no significant difference from expert counts (R2 = 0.97, p-value = 0.55, alpha = 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring.
外文关键词：deep learning;insect pest;mobile application;Sticky trap;mobile computing
作者：Hacinas, Eros Allan Somo;Querol, Lorenzo Sangco;Santos, Kris Lord T;Matira, Evian Bless;Castillo, Rhodina C;Arcelo, Mercedes;Amalin, Divina;Rustia, Dan Jeric Arcega
作者单位：Wageningen Univ & Res;Sultan Kudarat State Univ;De La Salle Univ;Bur Plant Ind Davao
期刊名称：AGRONOMY-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14（3）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台