PLANT MONITORING SYSTEM: AI SYSTEM FOR INTELLIGENT PLANT MONITORING

Abstract

Modern agriculture faces numerous challenges, such as water scarcity, climate change, and the need for accurate crop monitoring. Traditional monitoring methods often do not provide sufficient accuracy, which can lead to reduced yields and inefficient use of resources. The aim of the work is to develop and evaluate the effectiveness of the SmartPlant AI intelligent system for automated plant monitoring in order to optimize resource use, increase yields, and reduce the impact of the human factor. Methodology. The SmartPlant AI system combines IoT devices, cloud technologies, and artificial intelligence algorithms to analyze critical environmental parameters in real time. Sensors are used to measure soil moisture, temperature, lighting, and nutrient composition. ESP8266, ESP32, Raspberry Pi, and Arduino controllers transmit this data to the cloud via Wi- Fi or LoRaWAN using MQTT or HTTP API. Machine learning algorithms predict threats and generate recommendations, in particular, neural networks analyze the relationships between environmental parameters, regression models assess their impact on plant growth, and decision trees optimize irrigation and fertilization. The scientific novelty. The proposed system provides integration of IoT, cloud computing, and artificial intelligence for automated monitoring of plant health, which allows to increase the accuracy of agricultural technologies. The use of machine learning algorithms in the data processing process makes it possible to predict threats and adapt plant care to current conditions. Conclusions. The results of testing on the example of growing tomatoes showed that the SmartPlant AI system reduces water consumption by 31%, stabilizes soil moisture levels, and promotes faster plant growth by 34% compared to traditional methods. Automation of the plant care process allows to reduce the impact of the human factor, increase the efficiency of agricultural production, and minimize the risks associated with climate change. Further research may include integrating additional sensors, using drones and satellite data for even more precise management of agricultural processes.