An intelligent system for high-density small target pest identification and infestation level determination based on an improved YOLOv5 model

Abstract

Purpose:A deep learning-based intelligent system has been developed for the identification and detection of high-density small target pests with the aim of addressing the limitations observed in previous detection systems and manual sorting. The novel system promises to overcome issues of low detection accuracy, low detection efficiency, and serious leakage and error detection. It is expected to significantly improve the efficiency of pest detection, thereby offering a potential solution to the challenges posed by the presence of these pests in agricultural settings. Methods:In this paper, we propose an enhanced YOLOv5s model to tackle challenges in pest detection, such as small targets, high densities, and species diversity. Our method embeds Channel Attention (CA) modules into the algorithm and broadens the shallow feature detection scale of the original FPNet, bolstering small-target detection. Network convergence is accelerated using the Kmeans++ algorithm for prior frame generation and YOLOv5s’ intrinsic weights for transfer learning. The Efficient Intersection over Union (EIOU) loss function is adopted to address unbalanced data labels. Furthermore, we develop an algorithm to determine pest infestation levels, transforming detection results into pest grade evaluations. These advancements form the core of our proposed intelligent pest identification and detection system. Results:In our enhanced YOLOv5s model, we assessed the system’s performance using a dataset comprising six prevalent pest species. This dataset was specifically curated to emulate real-life detection scenarios, featuring small pest targets with similar appearances and imbalanced data labels. We compared our model with a common one-stage model and noted average improvements of 5.5%, 2%, and 3.95% in accuracy, recall, and detection precision respectively across both sparse and dense scenarios. Importantly, the enhanced model maintained a detection time of roughly 10ms per image and was only 1MB larger than the original model, illustrating a balance between accuracy and efficiency. Furthermore, our algorithm enables the assessment of pest grades based on the input detection results, illustrating the degree of pest infestation. Conclusion:The present paper describes the development of an intelligent system for the automatic identification and detection of pests. Experimental results demonstrate the high efficiency and efficacy of the proposed system in identifying and detecting common pests, even in complex scenarios characterized by high pest density and data label imbalance. Notably, the proposed system has the potential to assist practitioners in the fields of agriculture and forestry to make informed decisions on pest control, thereby improving work efficiency and productivity.