Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (20): 3926-3938.doi: 10.3864/j.issn.0578-1752.2022.20.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Improvement of Row Detection Method Before Wheat Canopy Closure Using Multispectral Images of UAV Image

MA Xiao1,2(),CHEN PengFei1,3()   

  1. 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences/State Key Laboratory of Resources and Environment Information System, Beijing 100101
    2University of Chinese Academy of Science, Beijing 100049
    3National Earth System Science Data Center, National Science and Technology Infrastructure of China, Beijing 100101
  • Received:2021-12-10 Accepted:2022-02-28 Online:2022-10-16 Published:2022-10-24
  • Contact: PengFei CHEN E-mail:maxiao20@mails.ucas.ac.cn;pengfeichen@igsnrr.ac.cn

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Abstract:

【Objective】In order to make precise management of wheat, it is of great significance to accurately identify the location of its rows. In this study, the aim was to improve the traditional Hough transform-based methods and the green pixel accumulation- based methods respectively, and then to analyze the different methods before and after the improvement for wheat row detection, so as to provide the technical support for accurate detection of wheat rows.【Method】A wheat water-nitrogen coupling experiment was established. During the early jointing stage, the multispectral images of wheat under different growth conditions was obtained by the RedEdge M sensors mounted on a four rotor unmanned aerial vehicle (UAV). First, based on these data, these images of the excess green minus excess red (ExGR) index were calculated, and then the Otsu method was performed on it to classify image pixels into vegetation and soil. Second, 3×1 line template was used for morphological opening operation to reduce boundary irregularity and remove noise. Third, according to the characteristics of the wheat rows in the image, the modified wheat row extraction methods were proposed based on the optimization of the peak detection process of the traditional Hough transform-based method and the angel detection process of the traditional green pixel accumulation-based method, respectively. Finally, the detection results before and after improving the two methods were compared with the result of visually interpreted wheat rows, and their performances were evaluated by using the detection rate and crop row detection accuracy (CRDA). 【Result】The combination of the ExGR index and OTSU method could accurately identify the vegetation pixels and soil pixels automatically, and the overall accuracy was 93.75% and the Kappa was 0.87. The morphological opening operation could remove the pattern noise and reduce the error of later crop row detection. The modified Hough transform-based method effectively improved the peak detection accuracy by restricting the peak detection range with prior knowledge; Compare with traditional Hough transform-based methods, the averaged detection rate has increased from 30% to 67%, and the averaged CRDA has increased from 0.22 to 0.44. The modified green pixel accumulation-based method investigated the accumulation characteristics of the green pixels of the entire image, which effectively improved the angel detection accuracy; Compare with traditional green pixel accumulation-based method, the averaged detection rate has increased from 14% to 93%, and the average CRDA has increased from 0.12 to 0.69. The identification accuracy of the four methods from high to low were the modified green pixel accumulation-based method, the modified Hough transform-based method, the traditional Hough transform-based method, and the traditional green pixel accumulation-based method. 【Conclusion】This study improved the traditional crop row detection methods, which provided the technical support for the identification of wheat rows with high planting density and small row spacing.

Key words: wheat, planting rows, Hough transform-based method, green pixel accumulation-based method, UAV image

Fig. 1

The winter wheat field experiment W1: 80% field capacity; W2: 60% field capacity; N1: 0; N2: 70 kg N·hm-2; N3: 140 kg N·hm-2; N4: 210 kg N·hm-2; N5: 280 kg N·hm-2"

Fig. 2

Unmanned aerial vehicle platform with one acquired image"

Fig. 3

Visual identified row line of wheat"

Fig. 4

Flow chart for Hough transform method before and after modification"

Fig. 5

Cumulative graph for the feature points of wheat rows on column orientation a: Unmanned aerial vehicle image of wheat;b: The image was rotated to make the direction of wheat row perpendicular to the direction of image row; c: The image was not rotated to make the direction of wheat row perpendicular to the direction of image row"

Fig. 6

Flow chart for green pixel accumulation method before and after modification"

Fig. 7

Unmanned aerial vehicle images of different nitrogen fertilization treatments a-e corresponding to N1-N5 nitrogen application amount"

Fig. 8

An example of the unmanned aerial vehicle image of a wheat plot and its corresponding vegetation/soil binary image a: Unmanned aerial vehicle image; b: Vegetation/soil binary image"

Table 1

Precision of classification of vegetation/soil"

分类结果
Classification result		 参考数据 Reference data
植被 Vegetation 土壤 Soil 总计 Summary 用户精度 User’s accuracy (%)
植被 Vegetation 1346 97 1443 93.28%
土壤 Soil 103 1654 1757 94.14%
总计 Summary 1449 1751 3200
生产者精度 Producer‘s accuracy (%) 92.89% 94.46%

Fig. 9

Vegetation/soil binary image before and after morphological operation a: Original vegetation/soil binary image; b: Morphological operation result"

Fig. 10

Results of identified feature points of wheat rows a: Vegetation/soil binary image; b: Feature point of wheat row"

Fig. 11

Detection rate and CRDA values of Hough transform method and green pixel accumulation method before and after modification under different water and nitrogen treatment a: Detection rate of Hough transform method before and after modification; b: CRDA values of Hough transform method before and after modification; c: Detection rate of green pixel accumulation method before and after modification; d: CRDA values of green pixel accumulation method before and after modification"

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