Design and implementation of a portable snapshot multispectral imaging crop-growth sensor
2024
Yongxian Wang | Yongxian Wang | Yongxian Wang | Yongxian Wang | Yongxian Wang | Yongxian Wang | Jingwei An | Jingwei An | Jingwei An | Jingwei An | Jingwei An | Jingwei An | Jianshuang Wu | Jianshuang Wu | Jianshuang Wu | Jianshuang Wu | Jianshuang Wu | Jianshuang Wu | Mingchao Shao | Mingchao Shao | Mingchao Shao | Mingchao Shao | Mingchao Shao | Mingchao Shao | Jiacheng Wang | Jiacheng Wang | Jiacheng Wang | Jiacheng Wang | Jiacheng Wang | Jiacheng Wang | Xia Yao | Xia Yao | Xia Yao | Xia Yao | Xia Yao | Xia Yao | Xiaohu Zhang | Xiaohu Zhang | Xiaohu Zhang | Xiaohu Zhang | Xiaohu Zhang | Xiaohu Zhang | Chongya Jiang | Chongya Jiang | Chongya Jiang | Chongya Jiang | Chongya Jiang | Chongya Jiang | Yongchao Tian | Yongchao Tian | Yongchao Tian | Yongchao Tian | Yongchao Tian | Yongchao Tian | Weixing Cao | Weixing Cao | Weixing Cao | Weixing Cao | Weixing Cao | Weixing Cao | Dong Zhou | Dong Zhou | Dong Zhou | Dong Zhou | Dong Zhou | Dong Zhou | Yan Zhu | Yan Zhu | Yan Zhu | Yan Zhu | Yan Zhu | Yan Zhu
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.
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