Creation and test of applied software of network of wireless sensors for agriculture

The article describes applied software of units of such complex hardware-software system, as plants` state monitoring system for application in agriculture and ecological monitoring. The mentioned system consists of data acquisition system in the form of wireless sensor network and adaptive part in the form of decision-making support system. The authors described main applied software of au- tonomous nodes of wireless sensor network and implementation of some program functions of decision-making support system. Wire- less sensor network includes many autonomous wireless sensors, so the main criteria during applied software creation was assuring the energy efficiency of operation of autonomous measuring nodes and network coordinator, and correct interaction of nodes within all network. As it is very difficult to perform testing of applied software of wireless nodes individually in field conditions, it was tested the network cluster, including hardware and software as a whole, in conditions like to applied task. The main parameters, which define the correctness of applied software operation, were estimated. These parameters include, for example, time of network selforganization, distance and quality of stable communication, time of autonomous operation of wireless nodes without charging batteries and so on. To create applied software for the decision-making support system, first of all, methods of plants` state diagnosing and estimating the factors, which influence the plant state, were developed. For this, the field experiments were conducted to determine sufficient dose of herbicide application and estimate the soil moisture using the chlorophyll fluorescence induction method. For processing measured data, several methods of machine learning were used, including neural network approach. Application of machine learning methods made it possible, on the base of acquired data, to make early diagnostics of influence of stress factors on the plant even before the appearance of visual manifestations of such negative influence and determine the decrease of soil moisture through the diagnostics of plant itself, and inform the user about this.