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Received：16 April 2019，
Revised：2019-9-21，
Accepted：28 September 2019，
Published：16 March 2020
移动端阅览
目的
2
针对农业机械化、信息化和智能化发展对棉花长势快速监测与评估提出的需求，提出一种新的技术途径，依据低空无人机UAV（unmanned aerial vehicle）遥感技术为苗期棉花株数快速估算和长势等级分类。
方法
2
基于高分辨率无人机多光谱遥感影像，利用光谱信息、空间位置及数学形态学信息，结合田间调查数据，引入Hough变换等数学形态学方法实现田间棉苗垄中心线的提取，利用支持向量机回归方法构建具有较好稳健性的棉株估算模型，并依据株数估算结果进一步实现了保苗率、壮苗率计算和整体长势等级分类。
结果
2
以田间实地测量得到的样方数据为参考，对模型棉株估算精度评估的结果显示：支持向量机回归模型得到的株数估算精度更高，优于对比模型，估算值与观测值之间的确定系数R
2
为0.945 6，均方根误差为0.510 7。以株数为指标评估得到的整体长势空间分布与地面样方调查情况一致，实验田块里弱、壮、旺苗的比例分别为14.50%、83.37%、2.13%，表明研究区棉田出苗整齐度较差，整体长势偏弱。
结论
2
本文建立的棉苗株数与数学形态特征的回归模型能有效识别棉苗株数并进行整体长势等级分类，可为精准田间管理提供依据，为无人机在农业中的应用提供参考。
Objective
2
The cotton plant number is a key indicator to evaluate the effect of cotton mechanized sowing at the seedling stage of cotton plant growth. The growth level classification is important in decision-making for water and fertilizer management in the later growth stages. Conventional approaches to the estimation of seedling plant number and growth level classification were often based on ground visual check and subjective evaluation by farming technicians
which can be time-consuming
laborious
error-prone
and difficult to meet the needs of precision field management
which is especially true in arid areas
such as in Xinjiang
China
due to the sparsely populated and desert-oasis environment. Although previous studies had investigated cotton growth monitoring
these studies mostly focused on a large area due to the limitations of image spatial resolution
and few studies had worked on the cotton plant number estimation of the seedling stage. Therefore
the objective of the present study is to develop an automated and accurate approach to estimating the cotton plant number and characterizing the growth status of the seeding stage cottons based on high-resolution imagery.
Method
2
Multispectral remote sensing images over the cotton field of the study area in Shihezi City
Xinjiang were collected by a Micro MCA12 snap multispectral camera (Tetracam
United States) mounted on a low-altitude unmanned aerial vehicle (UAV) platform. The camera can capture 12 channels images at the visible to near-infrared wavelengths. The UAV system flew approximately 30 m above the ground
and a grey board with 48% reflectance was used to conduct exposure correction of the camera before taking off. Three grey boards with reflectance of 3%
22% and 48% were setup inside the field of the study area during the UAV flights. After acquiring the images
we used the standard reflectance curves of the grey boards to calibrate the images and retrieved the reflectance images after the preprocessing. The images were mosaicked with the commercially available software Pixe4D Mapper to generate the reflectance images of the study area. A total of 35 ground samples of 1 m×1 m were collected and photographed synchronously with a digital camera vertically from the ground. Normalized difference vegetation index (NDVI) and visible brightness (VB) derived from the UAV images were segmented using the Otsu algorithm to extract vegetation pixels and eliminate the interference caused by the bright spots of plastic mulch film in the field left from previous farming work. Before the Hough transform was performed to obtain the central lines of crop rows
multiple successive morphological dilation and skeleton extraction of the binary image of vegetation were conducted to reduce computational complexity and avoid detection of false central lines. Buffer analysis was developed with the central lines of cotton rows to remove weeds
thereby improving the efficiency and accuracy of subsequent analysis. Consequently
cotton pixels were delineated and vectorized into cotton plant objects from which 18 morphological features were finally extracted from the binary image. A total of 1 046 cotton objects were extracted with the aid of in-situ measured data
and were divided into two subsets using the stratified sampling strategy:524 for training and 522 for testing. Random forest algorithm was adopted to extract optimal features from the 18 features using the training samples before the statistical model for the estimation of plant number was created
and a subset of feature variables was selected based on the importance analysis of out-of-bag (OOB) data of the random forest algorithm. The selected variables were utilized to build a support vector regression (SVR) model for estimating the number of cotton plants. Three ratio indices
namely
seedling proportion
seedling density
and healthy seedling proportion
were calculated based on the estimation of cotton plant number. Finally
the overall growth level was evaluated based on farming knowledge about the cotton plantation in the Shihezi area.
Result
2
The results showed that the SVR and support vector classification (SVC) models achieved high accuracy and good generalization ability under the same training and testing datasets. The SVR model performed better at the testing dataset (the determinant coefficient R
2
=0.940 1
root mean square error (RMSE)=0.592)
and training dataset (R
2
=0.945 6
RMSE=0.510 7)
while the SVC model achieved significantly inferior accuracies at the testing dataset (R
2
=0.922 7
RMSE=0.718 3) and at the training dataset (R
2
=0.918 9
RMSE=0.755 6). This difference in performance may be due to the combination of the skewness of the dataset and the difference between the classification and regression algorithms. Accuracy assessment indicates that the spatial distribution of the overall growth levels is consistent with the field check in the study area. The current limitation and possible improvement of the proposed method were discussed.
Conclusion
2
With the analysis of the correlation between cotton plant number and morphological features of the cotton objects in the UAV images with very high spatial resolution
the application of a low-altitude UAV platform integrated with a high-resolution multispectral sensor effectively identified the number of plants and classified the overall growth levels of cottons in the seedling stage. However
the proposed approach in this study aimed at monitoring seedling-stage cotton growth in the arid area with typical oasis agriculture may not be applicable for the other growth stages of cotton. This research provides a reference for field management and the application of UAV in precision farming. Future studies intend to focus on the estimation of the height of cotton plants using a low-altitude UAV system
which is extremely useful for evaluating the growth of the plant in the seedling stage and other growth stages.
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