微型电脑应用2026，Vol.42Issue(3)：40-43,4.
基于改进型YOLOv7算法的玉米雄穗识别检测
Identification and Detection of Maize Tassels Based on Improved YOLOv7 Algorithms
摘要
Abstract
Monitoring the growth status of maize during the tasseling period plays an important role in its final yield.A drone equipped with a visible light camera is used to capture RGB images of maize during the tasseling period.Its object detection al-gorithms are studied,and appropriate models are used to automatically identify maize tassels in the images.The images are cropped to obtain a size of 640×640 picture based on the original data,and the cropped images are used to construct the re-quired dataset for training and testing the object detection model.The YOLOv7 model is improved by adding a detection head and introducing SIoU loss function which are two improvement strategies for enhancing model performance.The improved model achieves an mean average precision(mAP)value of 97.2％in the test dataset,an increase of 2.7％compared to before the improvement.The experimental results show that the proposed improvement strategy can effectively improve the accuracy of YOLOv7 model in detecting maize tassels in remote sensing images,and can accurately complete the task of identifying and counting tassels.关键词
深度学习/目标检测/玉米雄穗识别/YOLOv7Key words
deep learning/object detection/maize tassel identification/YOLOv7分类
信息技术与安全科学引用本文复制引用
古明琦,班松涛,李琳一,胡冬,田明璐..基于改进型YOLOv7算法的玉米雄穗识别检测[J].微型电脑应用,2026,42(3):40-43,4.基金项目
上海市农业科技项目(沪农科创字[2022第4-1号]) （沪农科创字[2022第4-1号]）
上海市农业科学院卓越团队建设项目(沪农科卓[2022]015) （沪农科卓[2022]015）
