HMA-YOLO: a high precision and lightweight detection model of corn trumpet in corn precision pesticide application system
Chengxiang Zhang, Wenqiang Li, Lili Wu, Yuqing Xing, Xueli Qi

TL;DR
This paper introduces HMA-YOLO, a lightweight and accurate model for detecting corn trumpets in corn fields to improve pesticide application precision.
Contribution
The novel HMA-YOLO model integrates HCT, MBMS-FPN, and AMCCDH for improved detection accuracy and efficiency in corn trumpet detection.
Findings
HMA-YOLO achieves a [email protected] of 91.5%, precision of 89.8%, and recall of 83.7%.
The model operates at 128 FPS with a model size of 3.1 MB and 1.407M parameters.
HMA-YOLO outperforms mainstream detectors and is deployable on the NVIDIA Jetson Xavier NX platform.
Abstract
Pests and diseases significantly reduce the quality and yield of corn, while the corn precision pesticide application system is one of the effective measures to solve this problem. However, the detection of corn trumpets in complex farmland environments poses significant challenges due to the high color similarity between corn trumpets and the background, the small target size, and occlusion by corn leaves. In this paper, we propose a lightweight HMA-YOLO model to accurately detect corn trumpets in agricultural background based on YOLOv12n model. Firstly, The HCT structure that is based on CNN and Transformer architectures with assignable feature map channels is introduced into the backbone network to extract target feature information and enhance the ability of the model to distinguish between targets and backgrounds. Secondly, an efficient multi-branch and multi-scale feature pyramid…
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Taxonomy
TopicsSmart Agriculture and AI · Advanced Neural Network Applications · Fire Detection and Safety Systems
