Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 23-34.doi: 10.12133/j.smartag.2021.3.2.202104-SA003
• Topic--Application of Spatial Information Technology in Agriculture • Previous Articles Next Articles
Received:
2021-04-26
Revised:
2021-06-28
Online:
2021-06-30
Published:
2021-08-25
corresponding author:
Zhao ZHANG
E-mail:paulo.flores@ndsu.edu;zhao.zhang.1@ndsu.edu
About author:
Paulo FLORES (1979-), male, assistant professor, research interests is remote sensing technologies in agriculture. E-mail: Supported by:
CLC Number:
FLORES Paulo, ZHANG Zhao. Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms[J]. Smart Agriculture, 2021, 3(2): 23-34.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2021.3.2.202104-SA003
Fig. 1
Workflow of wheat plot lodging ratio detection under different mission heights for unmanned aerial systemsNote: Non-, light, and severe lodging represent 0%, <50%, and >50% crop lodged, respectively; SVM—support vector machine; RF—random forest; KNN—K nearest neighbors; three heights are 15, 46, and 91 m
Fig. 3
General procedure on the development of automatic plot image generationNote: (a) original image; (b) monochrome of excess green; (c) binary image after thresholding (0.01) excess green image (b); (d) relationship between x coordinates (horizontal direction) and sum of each column; (e) relationship between y coordinates (vertical direction) and the sum of each row; and (f) samples of automatically generated extracted images of the individual plots
Table 1
Features selected based on domain knowledge for wheat lodging ratio detection
Feature number | Feature | Description |
---|---|---|
1 | R | Red channel |
2 | G | Green channel |
3 | B | Blue Channel |
4 | H | Hue Channel |
5 | S | Saturation channel |
6 | I | Intensity channel |
7 | L | Luminosity channel |
8 | a | a channel |
9 | b | b channel |
10 | NDI | Normalized difference index |
11 | ExG | Excess green index |
12 | ExR | Excess red index |
13 | GLCM | Texture feature |
Fig. 4
Detection accuracy of different wheat lodging ratios based on color and textural features extracted from unmanned aerial systems images collected at three flight altitudes coupled with SVM, random forest (RF and KNN classifiers)Note: Bars for each group with different letters are significantly different by Tukey test (α = 0.05)
Fig. 5
Detection accuracy of different wheat lodging ratio based on color and textural features extracted from unmanned aerial systems images collected at three flight altitudes coupled with RF classifierNote: Bars for each group with the same letters are not significantly different by Tukey test (α = 0.05)
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