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Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning

文献类型： 外文期刊

作者： Zhang, Shanxin 1 ; Feng, Hao 1 ; Han, Shaoyu 1 ; Shi, Zhengkai 1 ; Xu, Haoran 1 ; Liu, Yang 2 ; Feng, Haikuan 2 ; Zhou, Chengquan 2 ; Yue, Jibo 1 ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr Minist Agr, Beijing 100097, Peoples R China

3.China Agr Univ, Key Lab Smart Agr Syst, Minist Educ, Beijing 100083, Peoples R China

4.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Peoples R China

5.Zhejiang Acad Agr Sci ZAAS, Inst Agr Equipment, Hangzhou 310000, Peoples R China

关键词： unmanned aerial vehicle; soybean; convolutional neural network; deep learning

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 1 期

页码：

收录情况： SCI

摘要： Soybean breeders must develop early-maturing, standard, and late-maturing varieties for planting at different latitudes to ensure that soybean plants fully utilize solar radiation. Therefore, timely monitoring of soybean breeding line maturity is crucial for soybean harvesting management and yield measurement. Currently, the widely used deep learning models focus more on extracting deep image features, whereas shallow image feature information is ignored. In this study, we designed a new convolutional neural network (CNN) architecture, called DS-SoybeanNet, to improve the performance of unmanned aerial vehicle (UAV)-based soybean maturity information monitoring. DS-SoybeanNet can extract and utilize both shallow and deep image features. We used a high-definition digital camera on board a UAV to collect high-definition soybean canopy digital images. A total of 2662 soybean canopy digital images were obtained from two soybean breeding fields (fields F1 and F2). We compared the soybean maturity classification accuracies of (i) conventional machine learning methods (support vector machine (SVM) and random forest (RF)), (ii) current deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50), and (iii) our proposed DS-SoybeanNet method. Our results show the following: (1) The conventional machine learning methods (SVM and RF) had faster calculation times than the deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50) and our proposed DS-SoybeanNet method. For example, the computation speed of RF was 0.03 s per 1000 images. However, the conventional machine learning methods had lower overall accuracies (field F2: 63.37-65.38%) than the proposed DS-SoybeanNet (Field F2: 86.26%). (2) The performances of the current deep learning and conventional machine learning methods notably decreased when tested on a new dataset. For example, the overall accuracies of MobileNetV2 for fields F1 and F2 were 97.52% and 52.75%, respectively. (3) The proposed DS-SoybeanNet model can provide high-performance soybean maturity classification results. It showed a computation speed of 11.770 s per 1000 images and overall accuracies for fields F1 and F2 of 99.19% and 86.26%, respectively.

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