Crops Diagnosis Using Hurst Exponent Values in Fields Image Analysis

Abstract

One of the branches of sustainable agriculture is the precision farming which assumes an individual approach to each plant. The main problem encountered by the precision agriculture is to quickly acquire and analyze good quality data assessing the condition of the crop. One of the fastest growing monitoring techniques is the analysis of images obtained from cameras placed on UAV. The studies used the chaos tools to determine Hurst exponent values received from images collected during UAV flights over the fields. The obtained results of image analysis indicated the presence of a strong dependency between the Hurst exponent values and state of crops. Images showed crops which are in good standing have been seen as strong organize objects represented by the mean Hurst exponent values from 0.8 to 0.87. Crops in which occurred the destruction of plants on the collected images were estimated by the Hurst exponent between 0.41 and 0.49 values, which indicates the presence of the characteristics of chaotic changes in the distribution of color attributes.

Drone Video Capture – A New Method in Precision Agriculture

“…The use of drones to collect visual information for the condition of arable crops is one of the modern activities as part of precision agriculture. The advantages and disadvantages of this method of crop monitoring have been known (Ekielski A. et al, 2017), (Van der Wal et al, 2013) and the advantages of small drones equipped with onboard sensors over satellites have been pointed out in details (Biserkov, 2017). For complex environmental tasks, quadcopters have been the most appropriate, (Rao Mogili U.…”

“…The possibility of describing the texture of the captured image using the fractal mathematical apparatus was presented (Ekielski A., et al, 2017), which could be applied as a measure of uniformity in the analysis. It was shown how, with the Hearst exponent, a dimensionless measure for evaluating recurring events that are sequential over time, the digital image could be represented as a 2D temporary process.…”