Abstract
An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.
Highlights
Wheat (Triticum aestivum L.), which is the oldest cultivated crop, is presently an important staple crop in China; improving the level of wheat production is critical to ensuring national food security and economic income to farmers [1]
This paper proposes a method for constructing a were divided into periods I and II according to the characteristics of wheat growth, and a growth wheat-growth-index spectral monitoring model based on the growth stages
The addition of a 660 nm band sensor to crop-growth monitoring and diagnosis (CGMD) increased the degree of freedom of vegetation index selection, and the three-band vegetation indices constructed in this paper improved the prediction accuracy of the growth indices leaf area index (LAI) and leaf dry weight (LDW)
Summary
Wheat (Triticum aestivum L.), which is the oldest cultivated crop, is presently an important staple crop in China; improving the level of wheat production is critical to ensuring national food security and economic income to farmers [1]. With the development of agricultural cultivation techniques and the improvement of the level of agricultural mechanization, wheat production and management has gradually shifted to being more precise and intelligent; effectively obtaining wheat growth information is an important prerequisite for precision agricultural operations [2]. The research results showed that the instrument could accurately obtain the chlorophyll index, flavonoid index, and nitrogen balance index of crop leaves, achieve the fast and non-destructive monitoring of crop growth, and help crop field nitrogen management. These two monitors have high accuracy, but can only monitor indices, such as the chlorophyll content of crop leaves, by point sources. They cannot represent the growth index of crop populations, and their application is limited
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