The article considers aspects of using the based on fuzzy logic ISA in agriculture. It is shown that in the real world knowledge of a qualitative nature, are often much more useful than quantitative knowledge. It shows on statements of fuzzy controllers, that the most of problems man must solve initially based on fuzzy knowledge. Especially for agricultural production, where a wide range of uncertainty dictated by the participation in technological processes of living organisms. Shows a brief sketch of the mathematical apparatus of fuzzy knowledge, with explanatory examples from the field of agriculture. It is shown that the characteristics of a fuzzy set acts as a accessory function. In addition to the fuzzy sets Apparatus uses the concepts of fuzzy variable and linguistic variable, which is at a higher level than fuzzy variable. There are over a dozen typical forms of curves to define the membership functions. The most used are: triangular, trapezoidal and Gaussian accessory functions. Globally the logical inference mechanism consists of four steps: introduction of fuzziness (phasification), fuzzy inference, composition and bringing to clarity, or dephasification. The most common method of inference in fuzzy controllers - Mamdani inference. It uses the min-max composition of fuzzy set to intelligent ACS had close to human ability to work with knowledge requires their formalization and representation in the technical system by means of a description language knowledge categories which the system could operate in the same way as people with words. It is also clear that to achieve greater effect of intellectualization of the technical system, this language must describe all possible types of knowledge: quantitative and qualitative, crisp and fuzzy. In conclusion given a brief review of the practical application of IMS on the fuzzy logic basis in agricultural production by bringing the calculated dependencies of the algorithm for temperature control using fuzzy modeling.