Spectral-spatial analysis of remote monitoring data to identify agrophytocenoses stresses of technological nature
Methods of spectral-spatial analysis are promising for the identification of technological stresses. The most common solution for interpreting the causes of stress is the use of machine learning technologies, namely neural networks. As at technological stresses in particular at chemical poisoning of crops, there can be various options of the coloring of the affected plants the possibility of providing a sufficient amount of initial data for training of neural networks is doubtful. An alternative is graph analysis of the distribution of stress areas on the field map. Given the urgency of the problem for promising technologies of precision agriculture, the work aimed to develop a spectral-spatial method of monitoring technological stresses, namely the algorithm and software for its.
Experimental studies of the manifestation of technological stresses on winter crops on the example of wheat and rapeseed were conducted during 2018-2020 in production fields using universal cameras in the visible range and special multispectral Slantrange systems.
For remote monitor the state of winter crops an algorithm for identifying technological stresses was developed, which is implemented in the developed software in Python for spectral-spatial analysis of stress index maps. It has been experimentally confirmed in the production fields that the use of the developed software allows identifying the contours of areas of plants with stresses of technological nature based on stress index distribution maps.