Digital Maize Crop Guardian: Automated Identification of Fall Armyworm Infestation using Computer Vision

Abstract

Identifying fall armyworm (FAW) infestation is critical for mitigating yield losses in maize crops. This study focuses on visually observable patterns indicative of FAW infestation on maize crops, amidst various biotic and abiotic stresses. With FAW emerging as a significant threat to maize cultivation, automated identification methods are imperative. Leveraging computer vision techniques, this paper proposes an algorithm designed to identify FAW infestation in maize crops. By analyzing images and identifying affected spots, the algorithm serves as a digital guardian for maize crops, aiding in timely intervention and management strategies.