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利用便携式三波段仪器监测小麦生长情况，用于作物生长监测和诊断。

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

机构信息

College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.

National Information Agricultural Engineering Technology Center, Nanjing, 210095, China.

出版信息

Sensors (Basel). 2020 May 20;20(10):2894. doi: 10.3390/s20102894.

DOI:10.3390/s20102894
PMID:32443796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7285128/
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 (R, R), and NDVI (R, R), respectively. R 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 R 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.

摘要

一种用于监测和诊断作物生长的仪器可以快速、无损地获取作物生长信息，有助于田间生产和管理。针对现有作物生长监测和诊断双波段仪器存在的信息量不足、部分生长指标反演精度低等问题，我们团队研发了一种便携式三波段作物生长监测与诊断仪（CGMD），以获取更多的信息。基于 CGMD，本文开展了小麦生长指标监测研究。根据获取的三波段反射光谱，通过不同波段组合、双波段植被指数（NDVI、RVI 和 DVI）和三波段植被指数（TVI-1 和 TVI-2）构建组合指数。CGMD 与商用仪器 FieldSpec HandHeld2 获得的植被指数拟合效果较好，新仪器可用于监测冠层植被指数。通过将每个植被指数拟合到生长指数，结果表明，与叶面积指数（LAI）、叶干重（LDW）、叶氮含量（LNC）和叶氮积累量（LNA）对应的最优植被指数分别为 TVI-2、TVI-1、NDVI（R，R）和 NDVI（R，R）。LAI、LDW、LNC 和 LNA 对应的 R 值分别为 0.64、0.84、0.60 和 0.82，相对均方根误差（RRMSE）值分别为 0.29、0.26、0.17 和 0.30。在 CGMD 中增加红光波段，可有效提高小麦 LAI 和 LDW 的监测结果。针对植被指数饱和问题，本文提出了一种根据生长时期构建小麦生长指数光谱监测模型的方法，提高了 LAI、LDW 和 LNA 的预测精度，R 值分别为 0.79、0.85 和 0.85，RRMSE 值分别为 0.22、0.23 和 0.28。提出的方法可为小麦田间栽培提供指导。

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本文引用的文献

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Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat.利用便携式主动传感器监测冬小麦生长参数并预测籽粒产量。
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Development of an Apparatus for Crop-Growth Monitoring and Diagnosis.
作物生长监测与诊断仪器的研制。
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