Problems of Multispectral Image Processing in Agriculture

This study provides a comprehensive comparative analysis of satellite-based and drone-based imaging platforms for agricultural monitoring, with particular emphasis on multispectral imaging capabilities. Our analysis reveals that while satellite systems offer broad coverage and cost-effectiveness for large-scale monitoring, drone-based platforms provide superior spatial resolution (up to 2.5 cm/pixel) and greater flexibility for targeted data acquisition, making them ideal for medium-sized agricultural plots. The research examines key imaging technologies and platforms, including the Sentinel-2 satellite system and drone-mounted sensors such as the MicaSense RedEdge-MX, evaluating their performance across critical agricultural applications. The paper further explores the implementation of convolutional neural networks for processing multispectral data, demonstrating their exceptional capability in performing crucial agricultural tasks including crop classification, disease detection, and stress assessment. By incorporating spectral indices, thermal indices and biophysical parameters (LAI, chlorophyll content) into neural network training, we develop a robust framework for agricultural monitoring and yield prediction. This research contributes both to the theoretical understanding of remote sensing in agriculture and provides practical guidance for implementing precision agriculture solutions that enhance productivity and sustainability in modern farming systems.