Estimating growth status of winter wheat based on aerial images and hyperspectral data

Abstract

The aim of this paper is to estimate the growth status and yield of winter wheat using aerial images and hyperspectral data obtained by unmanned aircraft, and then to perform precision management to the crop. The test farm was divided into 48 cells. Twenty-four cells were selected as variable rate fertilization area, and the other 24 cells were used as contrast area with low fertilization in growth season. In 2004, the aerial images of winter wheat canopy were measured from an unmanned aircraft. The SPAD value of crop leaf was acquired using a SPAD-502 chlorophyll meter, and then the hyperspectral reflectance of the crop canopy was measured by a handheld spectroradiometer. The vegetation indices, NDVI and DVI, were calculated from the hyperspectral data. The characteristics of the aerial images were used to evaluate the growth status. The RGB values of all cells were calculated from aerial images. The result showed that total nitrogen had better correlation with SPAD, NDVI, DVI, and RGB. NDVI and DVI had high correlation with the growth condition, and R/(R+G+B) and G/(R+G+B) had good correlation with the growth status and yield. The variable rate fertilization based on aerial images and NDVI was executed in the experimental cells. The yield map showed that the spatial variation of the yield was reduced and the total yield was increased. While in contrast cells, the spatial variation of the yield is greater than in experimental cells because of the spatial variation of the field nutrition. Therefore, it is practical to use aerial images and hyperspectral data of the crop canopy in estimation of the crop growth status.

Effectiveness of Chlorophyll Meter Measurement in Winter Wheat at Field Scale Level

“…In all cases, the errors represent a high percentage of nutrient variability in the field, making them ineffective. This analysis explains the discrepancy and helps in understanding the results of other authors and studies where the effects of using N-Tester were identified, but not in the expected magnitude (Han et al 2005;Liang et al 2004).…”

Farm Machinery and Processes Management in Sustainable Agriculture

“…Dammer et al (2015) applied NDVI (Normalised Difference Vegetation Index) to distinguish between wheat varieties with varying levels of resistance to Fusarium spp. Han et al (2005) obtained images from the UAS system for evaluation of the growth status of winter wheat. In crop monitoring, aerial imaging has an advantage over collecting data by means of small-scale satellite remote-sensing as well as by sensors mounted on ground-operating machines: the highresolution images taken from a relatively small height and at favourable atmospheric conditions provide a good insight into details and, at the same time, the general overview of the cultivated area (Bzowska-Bakalarz et al 2017.…”

Farm Machinery and Processes Management in Sustainable Agriculture, X International Scientific Symposium

“…Dammer et al (2015) applied NDVI (Normalised Difference Vegetation Index) to distinguish between wheat varieties with varying levels of resistance to Fusarium spp. Han et al (2005) obtained images from the UAS system for evaluation of the growth status of winter wheat. In crop monitoring, aerial imaging has an advantage over collecting data by means of small-scale satellite remote-sensing as well as by sensors mounted on ground-operating machines: the highresolution images taken from a relatively small height and at favourable atmospheric conditions provide a good insight into details and, at the same time, the general overview of the cultivated area (Bzowska-Bakalarz et al 2017, Lan et al 2010.…”