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Monitoring of soil parameters is important for farmers to make the right management decisions. This is especially useful for small farms, where the farmer needs to have up-to-date information about the condition of his land. Laboratory soil testing is a time-consuming and labor-intensive process. Therefore, the development of a mobile application for Android and iOS as a cyber-physical system for monitoring the main soil parameters (humidity, temperature, acidity, density) is important and useful. The data acquisition system proposed by the authors for a cyber-physical system that receives information from sensors ensures control over the processes of growing crops. User data and location information are stored in real time in the program’s cloud database. In order to predict the planned yield with the most economical use of available resources, it is important to choose the correct dates for sowing seeds and harvesting. Therefore, the authors proposed to implement a neural network in a cyber-physical system. A neural network model for predicting grain yields as part of a cyber-physical system is proposed. The authors conducted an analytical study of the influential factors on soil condition and the consequences that variations in these factors may lead.
