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. 2024 Aug 26:15:1416221.
doi: 10.3389/fpls.2024.1416221. eCollection 2024.

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor

Yongxian Wang  1   2   3   4   5   6 , Jingwei An  1   2   3   4   5   6 , Jianshuang Wu  1   2   3   4   5   6 , Mingchao Shao  1   2   3   4   5   6 , Jiacheng Wang  1   2   3   4   5   6 , Xia Yao  1   2   3   4   5   6 , Xiaohu Zhang  1   2   3   4   5   6 , Chongya Jiang  1   2   3   4   5   6 , Yongchao Tian  1   2   3   4   5   6 , Weixing Cao  1   2   3   4   5   6 , Dong Zhou  1   2   3   4   5   6 , Yan Zhu  1   2   3   4   5   6
Affiliations

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor

Yongxian Wang et al. Front Plant Sci. .

Abstract

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

Keywords: crop growth monitoring; field experiments; mosaic filter spectroscopy; portable snapshot multispectral imaging crop-growth sensor; prediction models; wide band co-optical path imaging system.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Portable snapshot multispectral imaging crop growth sensor (PSMICGS) hardware system. (A) MF1 band settings and transmittance curves for each band. (B) MF2 band settings and transmittance curves of each band. (C) Schematic diagram of the PSMICGS hardware system architecture. (D) Three-dimensional model of the entire PSMICGS machine structure. (E) Physical diagram of the entire PSMICGS machine structure.
Figure 2
Figure 2
PSMICGS software system. (A) Workflow of the software system and (B) diagram of the software interface.
Figure 3
Figure 3
Assembly and adjustment of the PSMICGS. (A) Three-dimensional diagram of the front imaging system: 1. Lens, 2. Front panel, 3. Component fixing device, 4. DM and mounting fixing device, 5. Second hexagon socket set screws with cup point, 6. Second fixing adjustment piece, 7. MMC2, 8. First hexagon socket set screws with cup point, 9. First fixing adjustment piece, and 10. MMC1. (B) On-site image of the front imaging system assembly and adjustment.
Figure 4
Figure 4
Spectral and radiometric calibration system of the PSMICGS. (A) On-site image of the spectral calibration. (B) System block diagram of the radiation calibration. (C) Site diagram of the setup for radiation calibration.
Figure 5
Figure 5
Radiometric response accuracy test of the PSMICGS. (A) Standard diffuse reflective panels with varying reflectivities, and (B) test site.
Figure 6
Figure 6
Research location and field experimental layout. (A) Layout of the wheat trial site. (B) Layout of the rice trial site. (C) Layout diagram of the PSMICGS collection of crop spectral images.
Figure 7
Figure 7
Checkerboard pattern status within the fields of view before and after the calibration of the front imaging system. Prior to calibration: (A) MMC1, (B) MMC2, (C) composite image of the checkerboard pattern at the middle position. Post-calibration: (D) MMC1, (E) MMC2, (F) composite image of the checkerboard pattern at the middle position, (G–J) composite images of the checkerboard pattern at the edge positions.
Figure 8
Figure 8
Image registration based on SIFT: (A) Reference image (MMC1), (B) image to be registered (MMC2), (C) feature matching graph, (D) mosaicked checkerboard image, and (E) image registration fusion graph.
Figure 9
Figure 9
Spectral calibration results of the PSMICGS: (A) Original response DN curves of each channel, (B) Gaussian fitted response DN curves of each channel, and (C) DN curves after spectral crosstalk correction.
Figure 10
Figure 10
Radiometric calibration results of the PSMICGS: (A–D) Linear fitting graphs of the response DN of the PSMICGS and the different radiance values for exposure times set to 60, 80, 100, and 120 ms.
Figure 11
Figure 11
Performance test results of the PSMICGS: (A) Signal-to-noise ratio (SNR) statistics for MMC1, (B) SNR statistics for MMC2, (C) test values for panels A1–A8 using the PSMICGS and ASD, and (D) relative error values between the PSMICGS and ASD.
Figure 12
Figure 12
Correlation of the PSMICGS-constructed VIs with rice and wheat AGB and LAI.
Figure 13
Figure 13
Prediction models of the rice and wheat AGB and LAI constructed based on the PSMICGS. (A, B) are rice AGB and LAI, respectively, and (C, D) are wheat AGB and LAI, respectively.
Figure 14
Figure 14
Scatter plots for the validation of the prediction models of rice and wheat AGB and LAI constructed based on the PSMICGS: (A, B) are the rice AGB and LAI, respectively, and (C, D) are the wheat AGB and LAI, respectively.
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