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Sensors 2018, 18(9), 3129; https://doi.org/10.3390/s18093129

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

1,2,3,4
,
5
,
1,2,3,4
,
1,2,3,4
and
1,2,3,4,*
1
College of Agriculture, Nanjing Agriculture University, Nanjing 210095, China
2
National Engineering and Technology Center for Information Agriculture, Nanjing 210095, China
3
Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095, China
4
Jiangsu Collaborative Innovation Center for the Technology and Application of Internet of Things, Nanjing 210095, China
5
Nanjing Institute of Agricultural Mechanization of National Ministry of Agriculture, Nanjing 210014, China
*
Author to whom correspondence should be addressed.
Received: 12 July 2018 / Revised: 29 August 2018 / Accepted: 13 September 2018 / Published: 17 September 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
Full-Text   |   PDF [3977 KB, uploaded 17 September 2018]   |  

Abstract

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively. View Full-Text
Keywords: crop growth index; canopy reflectance spectra; multispectral sensor; apparatus for monitoring and diagnosis; field experiment crop growth index; canopy reflectance spectra; multispectral sensor; apparatus for monitoring and diagnosis; field experiment
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Ni, J.; Zhang, J.; Wu, R.; Pang, F.; Zhu, Y. Development of an Apparatus for Crop-Growth Monitoring and Diagnosis. Sensors 2018, 18, 3129.

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