Remote field monitoring results feasibility assessment for energy crops yield management

Abstract

Most vegetation indices for UAV data analysis are developed for low-resolution satellite platforms, which requires the use of other monitoring methods and agrochemical measures to accurately determine the state of plantations, considering different stages of vegetation and spectral characteristics. The research aims to develop a methodology for assessing the suitability of remote sensing spectral data for energy crop nutrition management. The study was conducted using winter crops, including wheat and rapeseed. The results for winter wheat for the period from 2017 to 2020 were analysed. Stresses associated with nutrient deficiencies were studied in the fields of long-term stationary experiments at the National University of Life and Environmental Sciences of Ukraine. The results obtained from the Slantrange sensor and Slantview software were used. The studies confirmed that the pixel distribution in images of plantations (wheat and winter rape) can be described by a Gaussian distribution. The coefficient of determination for wheat was higher than for rape due to the peculiarities of the plant leaf structure. For rapeseed, a higher coefficient of determination was found for the lognormal distribution, which is not convenient for automating fertilisation processes in precision farming technologies. The analysis of the distribution by spectral channels, in particular the presence of several maxima, may indicate the presence of foreign inclusions or transitional stages of vegetation, which makes such data unsuitable for crop management. It has been established that if, after soil filtration, the maximum amplitude of the distribution exceeds the nearest one by more than 3 times, the growing season can be considered stable for a particular area, and the results of spectral monitoring are reliable for further analysis It has been confirmed that the vegetation indices GNDVI and RNDVI are not effective for assessing the reliability of data based on the standard deviation of the distribution. Reference values of the standard deviation of the distribution can be established at research stations with controlled stress factors, which will help in crop management