Computational neural network for processing light-reflective spectra of plants and remote phytosanitary monitoring of potatoes

The article is devoted to studying the possibility of using the WaveLetNN artificial neural network to analyze the results of remote phytosanitary monitoring of early detection of plants in potato plantings affected by late blight. Various methods for analyzing the spectral characteristics of plant reflection are considered, including the classification method. To detect plants infected with late blight, the WaveLetNN neural network analyzes the light reflective characteristics of potato plants obtained as a result of research (in the range of 300–1100 nm) and calculates the cognitive significance index (CSI = 0...10), which characterizes the intensity of biochemical processes inside plants aimed at countering phytopathogenic microflora. It was found that a significant increase in the CSI index signals infection of plants by phytopathogenic microflora and activation of protective biochemical processes on the part of plants. To reliably indicate infected plants, the WaveLetNN neural network underwent test training on a large number of light reflectance spectra of uninfected plants and plants artificially infected with late blight. The spectral reflectance characteristics of infected and uninfected plants were measured during 3, 4, 7 and 8 days after infection. Processing the obtained spectra using the WaveLetNN neural network made it possible to identify significant differences between the second- and third-order spectral characteristics of uninfected and late blight infected plants on the third day after infection. Moreover, for infected plants the CSI index values were 6.1...6.7, and CSI for healthy plants – 1.9...2.5. The Wave-LetNN neural network eliminates the influence on the light reflectance spectra of the spatial arrangement of plant leaves, unevenness of the soil surface and shading of individual sections of the field, normalizing the spectra to the total intensity of light reflected from the leaves. Thus, the WaveLetNN neural network can be used as the software core of online systems for remote phytosanitary monitoring of potato plants.