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Traditionally decision of agrophysics problems was evaluated with problem-oriented models. Utilization of fuzzy computing gives new dimension for agrophsics. It is well known that in agricultural sciences there exists much fuzzy knowledge, that is, knowledge which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic in nature. Fuzzy Computing is power tools for dealing with randomness and uncertainties. This paper reviews exploitation of fuzzy computing in agrophysics. The emphasis is on the achievements of Agrophysical Research Institute, St. Petersburg, Russia, especially in the fields of soil tillage, crop management, precision agriculture, melioration, and agricultural engineering.

Zoning of agricultural fields is an important task for utilization of precision farming technology. A method based on fuzzy indicator model theory for definition of zones with different levels of productivity is considered. Fuzzy indicator model for identification of zones with different levels of productivity is based on two general types of fuzzy indicators: the individual fuzzy indicator (IFI) type and the combined fuzzy indicator (CFI) type. IFIs are defined as a number in the range from 0 to 1, which reflect an expert concept and are modeled by an appropriate membership function. CFI is defined using fuzzy aggregated operations. The theoretical considerations are illustrated with this example based on data collected from a precision agriculture study in central Texas, USA. Soil samples were collected at different points, taking into account the actual longitude and latitude for each of these points. Because the experimental data (as in many cases) contained information only about a limited number of parameters, the calculations were restricted in this study. In this study, the parameters grain yield, total carbon (C), total nitrogen (N), and available phosphorus (P) were utilized. Using the author’s computer program, fuzzy indicators IFI/CFI was calculated for each zone separately. Utilizing results of calculations maps of zones with different levels of productivity were built

Assessment of sowing material is a significant concern in seed science. A promising tool for assessing seed material is Corona Discharge Photography or Gas Discharge Visualization (GDV). In this study, this tool was applied to determine relationships between sowing material characteristics and GDV parameters; an Adaptive Neuro-Fuzzy Inference System (ANFIS) was utilized to interpret the experimental data. By using ANFIS, a three input fuzzy inference system was constructed to define the contiguous relations between GDV parameters (i.e., glow area and shape factor) and root length.