AUTOMATED CONTROL SYSTEMS FOR UNMANNED PLATFORMS FOR AGRICULTURAL DATA COLLECTION: ARCHITECTURE, SOFTWARE, DEVELOPMENT PROSPECTS

Abstract

The article is devoted to the study of the architectural principles and software and hardware of automated control systems for unmanned platforms for collecting, processing and analyzing data in agriculture. Particular attention is paid to the use of UAVs for precision agriculture, which provides monitoring of agricultural landscapes and optimization of agricultural processes. Purpose of the study. The purpose of the work is to analyze and evaluate architectural solutions and software and hardware for the effective functioning of automated UAV control systems aimed at monitoring and analyzing agro-technological parameters in agriculture. Materials and methods. The study used UAVs equipped with RGB cameras, multispectral and infrared sensors, thermal imagers, laser scanners and radar systems. The methods of photogrammetry, analysis of vegetation indices (NDVI, GNDVI, ExG, ExGR), convolutional neural networks (CNN) and deep learning for image processing were applied. Training samples were formed using data generators, and processing was carried out using specialized software integrated with GIS and cloud computing. Scientific results obtained. A prototype of an automated system based on UAVs was developed, capable of detecting phytopathological changes, assessing the state of soils and vegetation with an accuracy of spatial extension of 1–10 cm. A neural network model (InceptionV2) was implemented, which provides high-precision image classification, in particular for diagnosing plant diseases. The use of vegetation indices and heat maps made it possible to create cartograms of crop conditions and predict yield. The system demonstrated effectiveness in monitoring soil moisture, plant stress, and soil texture, contributing to the adoption of informed agrotechnological decisions. Prospects for further research. Further research involves improving sensor systems, implementing autonomous UAV swarms, integrating with IoT, and developing artificial intelligence algorithms for predicting agricultural parameters. It is planned to scale solutions for large agricultural areas and increase economic efficiency by optimizing monitoring and management costs.