Multivariate Index Model for Vegetation Condition Assessment Using Fuzzy Logic

This paper presents an approach to crop condition assessment based on multi-index analysis using fuzzy logic. The aim of the study is to develop a methodology for integrated evaluation of agroecosystem conditions using spectral indices NDVI, NDRE, NDWI, and SAVI, which enables consideration of various physiological aspects of plant development. Proposed methodology is based on the processing of multispectral remote sensing data, calculation of vegetation indices, their normalization, and subsequent fuzzification using membership functions. The integration of indicators is performed through a fuzzy rule base followed by defuzzification to obtain a generalized assessment of field condition. An algorithm has been developed that includes the stages of index calculation, fuzzification, application of fuzzy rules, and defuzzification. The results demonstrate the capability of forming an integral assessment of vegetation condition considering biomass, chlorophyll activity, water balance, and soil background effects. The resulting generalized model allows for the identification of spatial variability within the field and the delineation of zones with different levels of vegetation development. Practical significance lies in the applicability of the proposed methodology in precision agriculture systems for decision support in crop management, optimization of input application, and improvement of agricultural production efficiency. The methodology is universal and can be implemented both in Python-based environments and in modern geographic information systems.