Convolutional Neural Network Based Fall Armyworm Damage Detection System

Abstract

Fall armyworm (Spodoptera frugiperda) is an invasive pest that attacks a wide range of plants (Early et al., 2018) It is especially notorious for attacking one of Africa’s most important foods: maize which is a source of livelihood and a staple food for millions of people across the continent. (Day et al., 2017). Current approaches used in fall armyworm monitoring require physical presence of an agricultural expert (agricultural extension officer or plant entomologist) to guide farmers in the identification of fall armyworm damage on maize leaves. Without expert training, farmers could easily confuse FAW attacks with other common maize pests leading to delayed or incorrect intervention measures and can lead to the loss of an entire crop. Meissle et al. (2010). In the recent past, machine learning techniques have been applied in pest detection. (Ebrahimi et al., 2017; Voulodimos et al., 2018). Despite the potential benefits offered by current machine learning approaches in literature, there lacks a CNN based mobile artifact that offers an easy-to-use alternative to classify and localize fall armyworm damage on maize leaves in the natural farm environment. This research compares the performance of two one stage convolutional neural network metaarchitectures to develop a FAW damage detection mobile application. Experimental results show impressive performance, with the best performing efficientdet lite model achieving a mean average precision of 85.85% and the best performing yolov4 tiny model achieving a mean average precision of 82.5%.