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High resolution hyperspectral imagery to assess wheat grain protein in a farmer's field

By: Rodrigues, F.
Contributor(s): Ortiz-Monasterio, I | Zarco‑Tejada, P.J | Toledo, F.H | Schulthess, U | Gerard, B | International Conference on Precision Agriculture St. Louis, Missouri USA July 31 - August 4, 2016.
Material type: materialTypeLabelArticlePublisher: Missouri, USA : International Society of Precision Agriculture, 2016Subject(s): Wheat | Grain AGROVOCOnline resources: Access only for CIMMYT Staff In: 13th International Conference on Precision Agriculture p. 1-13Summary: The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexibility of acquiring images from both manned and unmanned vehicles. For this reason, CIMMYT's research agenda aims at developing new crop management practices using PA/RS technologies. As part of these efforts, a wheat experiment was established on a farmer's field in the Yaqui Valley, northwestern Mexico, sown in January and harvested in May 2014. This work focuses on the evaluation of narrow-band physiological spectral indices to estimate wheat grain protein content (GPC). Also to determine the optimum normalized difference spectral index (NDSI) and ratio spectral index (RSI), aiming to better explore the use of the hyperspectral signal on the assessment of GPC. A weekly/biweekly flight campaign took place from GS31 stage (stem elongation) until harvest, totaling 10 airborne images acquired at high resolution with a micro-hyperspectral imaging sensor ranging from 400-850 nm region, flying at 1200 m above ground resulting in a ground resolution of 1 m. Manual grain sampling took place just before harvest through a targeted grid of 14 sampling points on block A and a half regular / half stratified grid of 50 sampling points each on block B. Under the conditions of this study, characterized by low spatial variability within the commercial field, the results obtained yielded coefficients of determination among vegetation indices (VIs) and GPC ranging from non-significant to 0.14 across all images. Complete two by two combinations of wavelengths approach applied into NDSI formula performed better on assessing GPC than VIs from the literature. However, the spectral region beyond the visible and near-infrared might be needed to assess GPC at field level. On the other hand, this approach allowed visualizing the spectral range/wavelengths that predominantly better explained GPC across the crop cycle than ordinary VIs.
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The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexibility of acquiring images from both manned and unmanned vehicles. For this reason, CIMMYT's research agenda aims at developing new crop management practices using PA/RS technologies. As part of these efforts, a wheat experiment was established on a farmer's field in the Yaqui Valley, northwestern Mexico, sown in January and harvested in May 2014. This work focuses on the evaluation of narrow-band physiological spectral indices to estimate wheat grain protein content (GPC). Also to determine the optimum normalized difference spectral index (NDSI) and ratio spectral index (RSI), aiming to better explore the use of the hyperspectral signal on the assessment of GPC. A weekly/biweekly flight campaign took place from GS31 stage (stem elongation) until harvest, totaling 10 airborne images acquired at high resolution with a micro-hyperspectral imaging sensor ranging from 400-850 nm region, flying at 1200 m above ground resulting in a ground resolution of 1 m. Manual grain sampling took place just before harvest through a targeted grid of 14 sampling points on block A and a half regular / half stratified grid of 50 sampling points each on block B. Under the conditions of this study, characterized by low spatial variability within the commercial field, the results obtained yielded coefficients of determination among vegetation indices (VIs) and GPC ranging from non-significant to 0.14 across all images. Complete two by two combinations of wavelengths approach applied into NDSI formula performed better on assessing GPC than VIs from the literature. However, the spectral region beyond the visible and near-infrared might be needed to assess GPC at field level. On the other hand, this approach allowed visualizing the spectral range/wavelengths that predominantly better explained GPC across the crop cycle than ordinary VIs.

Wheat CRP FP4 - Sustainable intensification of wheat - based cropping systems

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