No abstract

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.

The authors reviewed the types of network testing. The most common are simulation modeling, mock-up modeling, and full-scale experiments. It was examined existing software environments for simulation and debugging kits for mock testing. The main definitions and terms on the theory of experiment planning are given. According to the theory of experiment planning, the authors developed a plan for conducting a full-scale experiment and defined an algorithm for testing a wireless sensor network for express diagnostics of plants' state. Network testing parameters are the quality of network communication; network formation time; network communication distance; and battery life of sensors. The preparation and process of conducting a full-scale experiment for testing a WSN sample is described in detail. Wireless sensor network testing was carried out by different clusters, at different distances in five stages. During the experiment on testing the WSN, the operation of individual network nodes was checked, and the operation of the network as a whole. During the testing period, no abnormal operation of the sensors and the coordinator was revealed. 82 measurements were made in just five stages. The communication quality of the wireless sensor network has been checked. When testing the network, the transmission of data packets from the sensors to the coordinator was mostly successful. The integral estimate of unsuccessful data transmission sessions in the network was calculated. The communication range of the network at a distance of 20, 30, 40 and 60 m was checked. A graph of the dependence of the sensor signal power on the distance was built. Statistics were obtained on the decrease in the battery charge for each sensor. Based on the results of a full-scale experiment, the operation of a wireless sensor network for express diagnostics of the state of plants is considered successful. Keywords: Zigbee, wireless sensor network, sensor, full-scale experiment.

The authors made a review of "Information technology of express-estimation of plant state in large territories in stressful environment." The essence of digital agriculture and its main components are briefly described. The main part of the article describes the cluster of Wireless Sensor Network. The main components of the cluster and the principle of the cluster are given. The work of the cluster is based on the "Information technology of express-estimation of plant state in large territories in stressful environment." This technology is based on the method of chlorophyll fluorescence induction. The introduction of new information technology into industrial digital agriculture will make it possible to determine in real time the condition of plants suffering from the influence of one or another stress factor and develop an appropriate managerial decision to compensate the influence of a certain factor. The main technical requirements for the wireless node of the cluster are the ability to work in the field conditions; easy location on the plant; low cost; lightweight up to 25 g, small size, etc. The WSN cluster is intended for use in the agricultural sector and for environmental monitoring. Using the data collected by the cluster and an express analysis of the state of plants is carried out, which allows making the necessary managerial decision on the use of fertilizers, fungicides, pesticides, herbicides and the need for irrigation. The authors took into account that the cultivation of corn for grain occupies a large sector in the agrarian sector of Ukraine and is an urgent task. The authors analyzed the industrial technology of growing of crop for grain and it was adapted for the information technology for measuring the CFI. The main points of the technological process for the use of the WSN cluster in industrial agriculture on the example of corn are determined, and on their basis, a scheme for measuring the CFI of plants by the WSN cluster was developed. A brief step-by-step methodology has been developed for using the WSN cluster in measuring the CFI of corn. The authors also presented an analysis of energy consumption in the WSN and proposed the ways to improve the energy efficiency of the WSN nodes. Keywords: sensors, wireless sensor network, express diagnostics of plant, smart agriculture.