Automatic Crop Pest Detection Oriented Multiscale Feature Fusion Approach.

Abstract

Simple SummaryMonitoring pests is a labor-intensive and time-consuming task for agricultural experts. This paper proposes a new approach to classifying and counting different categories of crop pests. Specifically, we propose a multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network and a novel adaptive feature region proposal network. The multiscale feature pyramid network is used to fuse the multiscale pest information, which significantly improves detection accuracy. The adaptive feature region proposal network addresses the problem of not aligning when region proposal network (RPN) iterating, especially for small pest objects. Extensive experiments on the multi-category pests dataset 2021 (MPD2021) demonstrated that the proposed method provides significant improvements in terms of average precision (AP) and average recall (AR); it outperformed other deep learning-based models.Specialized pest control for agriculture is a high-priority agricultural issue. There are multiple categories of tiny pests, which pose significant challenges to monitoring. Previous work mainly relied on manual monitoring of pests, which was labor-intensive and time-consuming. Recently, deep-learning-based pest detection methods have achieved remarkable improvements and can be used for automatic pest monitoring. However, there are two main obstacles in the task of pest detection. (1) Small pests often go undetected because much information is lost during the network training process. (2) The highly similar physical appearances of some categories of pests make it difficult to distinguish the specific categories for networks. To alleviate the above problems, we proposed the multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network (MFPN) and a novel adaptive feature region proposal network (AFRPN). MFPN can fuse the pest information in multiscale features, which significantly improves detection accuracy. AFRPN solves the problem of anchor and feature misalignment during RPN iterating, especially for small pest objects. In extensive experiments on the multi-category pests dataset 2021 (MPD2021), the proposed method achieved 67.3% mean average precision (mAP) and 89.3% average recall (AR), outperforming other deep learning-based models.