智慧农业（中英文）2021，Vol.3Issue(2)：23-34,12.DOI:10.12133/j.smartag.2021.3.2.202104-SA003
基于无人机图像以及不同机器学习和深度学习模型的小麦倒伏率检测
Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms
摘要
Abstract
Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset generation consisting of plot images. Considering the manual method being inefficient, inaccurate, and subjective, this study developed a new image pro-cessing-based approach for automatically generating individual field plot datasets. Images from wheat field trials at three flight heights (15, 46, and 91 m) were collected and analyzed using machine learning (support vector machine, random forest, and K nearest neighbors) and deep learning (ResNet101, GoogLeNet, and VGG16) algorithms to test their performances on detecting levels of wheat lodging percentages: non- (0％), light (<50％), and severe (>50％) lodging. The results indicated that the images collected at 91 m (2.5 cm/pixel) flight height could yield a similar, even slightly higher, detection accuracy over the images collected at 46 m (1.2 cm/pixel) and 15 m (0.4 cm/pixel) UAS mission heights. Comparison of random forest and ResNet101 model results showed that ResNet101 resulted in more satisfactory performance (75％ accuracy) with higher accuracy over random forest (71％ accuracy). Thus, ResNet101 is a suitable model for wheat lodging ratio detection. This study recommends that UASs images collect-ed at the height of about 91 m (2.5 cm/pixel resolution) coupled with ResNet101 model is a useful and efficient ap-proach for wheat lodging ratio detection.关键词
小麦倒伏率/机器学习/深度学习/数据采集高度/无人机/ResNet101Key words
wheat lodging ratio/machine learning/deep learning/mission height/UAS/ResNet101分类
信息技术与安全科学引用本文复制引用
Paulo FLORES,张昭..基于无人机图像以及不同机器学习和深度学习模型的小麦倒伏率检测[J].智慧农业（中英文）,2021,3(2):23-34,12.基金项目
North Dakota Agricultural Experiment Station Precision Agriculture Graduate Research Assistantship(6064-21660-001-32S) （6064-21660-001-32S）
USDA Agricultural Research Service Project(435589) （435589）
