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Sensors 2016, 16(4), 437; doi:10.3390/s16040437

Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors

1
College of Information Engineering, Northwest A&F University, Yangling 712100, China
2
Institute of Water Saving Agriculture in Arid Area of China, Northwest A&F University, Yangling 712100, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 22 January 2016 / Revised: 12 March 2016 / Accepted: 22 March 2016 / Published: 25 March 2016
(This article belongs to the Special Issue Sensors for Agriculture)
View Full-Text   |   Download PDF [1984 KB, uploaded 25 March 2016]   |  

Abstract

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis. View Full-Text
Keywords: winter wheat; leaf chlorophyll content; visible and near infrared sensors; agricultural information acquisition; partial least squares winter wheat; leaf chlorophyll content; visible and near infrared sensors; agricultural information acquisition; partial least squares
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, J.; Han, W.; Huang, L.; Zhang, Z.; Ma, Y.; Hu, Y. Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors. Sensors 2016, 16, 437.

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