Linear Transects of Imagery Increase Crop Monitoring Efficiency Using Fixed-Wing Unmanned Aircraft Systems

Crop monitoring with unmanned aircraft must be timely to prevent yield losses and have low cost to be profitable. However, the expenses of acquiring large numbers of images with large overlap and creation of ortho-rectified mosaics may make unmanned aircraft system (UAS) monitoring slow and cost prohibitive. A fixed-wing UAS was flown over six center-pivot irrigated fields and untilled sagebrush steppe over the 2014 growing season. A 90-m by 90-m area centered at the image nadir point was analyzed as a single point along a linear transect. When aggregated by field, UAS normalized difference vegetation index (NDVI) agreed with NDVI from a corresponding Landsat image, with an R-2 of 0.958 and an RMSE of 0.058. Since each image retained its high spatial resolution, the proposed transect method could be used along with computer vision and artificial intelligence to detect plant stress, weeds, pests, and diseases.