Intelligent Field Sensor Station for Monitoring Agrophysical Parameters and Phenotyping in Precision Agriculture System

Abstract

Current trends in agriculture highlight the widespread adoption of information technology and Internet of Things (IoT) sensor networks for monitoring agrophysical soil parameters and phenotyping objects. This approach enables precise, real-time data analysis, optimizing agricultural processes and supporting the development of adaptive management systems. The integration of information technology with the monitoring of agrophysical parameters and phenotyping objects underscores the strategic importance of this approach, especially in the context of climate variability and the growing need to enhance production sustainability. (Research purpose) To develop an intelligent field sensor station for precision farming that ensures high-accuracy, real-time monitoring of agrophysical parameters and plant phenotyping using an Internet of Things sensor network. (Materials and methods) Existing methods for monitoring agrophysical parameters and phenotyping objects were analyzed. Based on these methods, a design for an intelligent field sensor station was developed, and suitable sensors were selected. (Results and discussion) The intelligent field sensor station successfully demonstrated its efficiency, confirming both its functionality and reliability in simultaneous data collection. The data collected on soil agrophysical parameters, meteorological conditions and plant phenotyping provide extensive knowledge for precision farming and optimizing agricultural processes. (Conclusions) Light gray forest soil with high porosity and neutral pH level provided favorable conditions for crops. Preliminary chemical analysis of the soil revealed moderate levels of organic matter, mobile phosphorus, and potassium, indicating a potentially fertile site. Meteorological data playeda key role in agrometeorological analysis, significantly impacting agricultural processes. The developed station introduces an innovative approach to monitoring agricultural parameters, offering promising prospects for modern agriculture.