Field detection of pests based on adaptive feature fusion and evolutionary neural architecture search

Accurate detection of pests is vital in smart agriculture as it is among the main factors that profoundly influence the yield and quality of crops. In the actual field, pests frequently manifest as small objects, thereby presenting a considerable obstacle to effectively detect pests in the field. For the problem of ineffective utilization of plant context information and inadequate design of neural architecture in field pest detection, we propose the pest detection model (PestNAS) based on adaptive feature fusion and evolutionary neural architecture search. It consists of the adaptive feature fusion module: plant context information is extracted, and the adaptive fusion of pest-related features and plant auxiliary features is designed to effectively utilize plant information; the evolutionary search space module: the novel search space that includes resolution and receptive field enhancement operations is designed with evolution to improve pest representation; the GA-Adam search algorithm: the Adam with genetic algorithm is designed to optimize the objective function of neural architecture search and obtain the relatively better neural architecture for pest detection. The ablation experiments verify the effectiveness of each module in the PestNAS. The comparison experiments reveal that the PestNAS can achieve higher detection accuracy than the other ten neural architecture search models on eleven field pests.