Detection of Water Content in Lettuce Canopies Based on Hyperspectral Imaging Technology under Outdoor Conditions

Jing Zhao, Hong Li (), Chao Chen, Yiyuan Pang and Xiaoqing Zhu
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Jing Zhao: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Hong Li: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Chao Chen: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Yiyuan Pang: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Xiaoqing Zhu: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 11, 1-21

Abstract: To solve the problem of non-destructive crop water content of detection under outdoor conditions, we propose a method to predict lettuce canopy water content by collecting outdoor hyperspectral images of potted lettuce plants and combining spectral analysis techniques and model training methods. Firstly, background noise was removed by correlation segmentation, proposed in this paper, whereby light intensity correction is performed on the segmented lettuce canopy images. We then chose the first derivative combined with mean centering (MC) to preprocess the raw spectral data. Hereafter, feature bands were screened by a combination of Monte Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighting sampling (CARS) to eliminate redundant information. Finally, a lettuce canopy moisture prediction model was constructed by combining partial least squares (PLS). The correlation coefficient between model predicted and measured values was used as the main model performance evaluation index, and the modeling set correlation coefficient R c was 82.71%, while the prediction set correlation coefficient R P was 84.67%. The water content of each lettuce canopy pixel was calculated by the constructed model, and the visualized lettuce water distribution map was generated by pseudo-color image processing, which finally revealed a visualization of the water content of the lettuce canopy leaves under outdoor conditions. This study extends the hyperspectral image prediction possibilities of lettuce canopy water content under outdoor conditions.

Keywords: hyperspectral imaging; outdoor conditions; preprocessing; feature selection; water content prediction; lettuce (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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