Machines, allowing the application of relevant technologies or restricting the erroneous activities of the farmer, bring knowledge to farms. These machines reduce the effort of work and meet the requirements formulated by Professor R. Michalek and Professor J. Kowalski in the research into the scientific and technical progress in the agricultural industry. The cognitive objective has been associated with the evaluation of the machines in the context of the problems of knowledge distribution. The observations of orchards in the Nowy Sącz District have provided some assistance, based upon the example of which, the following matters have been considered, notably: - the issues of farms modelling, as the overall grounds for further analyzes; - the possibilities of attaining the cognitive objective, as the detailed subject. The conceptual modelling has been proposed, in accordance with principles used in the IT science and corresponding to the UML language philosophy. The difficulties seen in farms modelling arise from the dissipation of knowledge, its size, and the necessity of specifying numerous, related structures. The ontology issues have created the necessity of establishing the base, acting as the prime, universal cognitive backbone. Some proprietary solutions have also been proposed. The following notions have been introduced to facilitate the realization of the detailed subject: - The declarative knowledge, related to the implementation of a specific technology by the machine; - The procedural knowledge, allowing the machine to respond depending on the situation it has recognized; - The declarative or procedural knowledge, describing the machine capabilities of interacting with other systems, in particular, with its operator. This has been called the potential for interaction. The types of declarative and procedural machines, as well as combined types and finely machines with potential for interaction have been relevantly recognized. The focus has chiefly been on the declarative type, the most popular in the solutions currently applied. The hypothesis about the possibility of determining the amount of knowledge that characterizes these machines in a formal way has been confirmed. The proprietary methods of evaluating this amount of knowledge based upon cognitive modelling, symbolic models of physics and qualitative interpretation of phenomena have been presented. The assumptions adopted have set out the relevant perspective, physical situation and the original space with the specific inception entropy. The method has been illustrated with the examples from the orchards sector. The problem of the "amount" of knowledge, the carrier of which machines are, was not considered in the agricultural industry, and the results are part of the cognitive model of the farm. The procedural knowledge features different and diversified character, mostly corresponding to the algorithmic issues with a certain computation complexity. The behavioural models assisted assessments of information processing by individuals are problematic and difficult. The arguments and examples documenting the necessity of further works have been presented. It is doubtless that the designers and researchers should aim at incorporating the aspects of shaping and distribution of knowledge between the farmer and the farming machine in a way that is less intuitive and more formal.