Remote Sensing and Deep Learning-Based Image Classification for Precision Agriculture

Abstract

This Precision agriculture has become an essential approach to modern farming, aiming to optimize resource use, improve crop productivity, and ensure sustainability. A cornerstone of precision agriculture is the ability to collect, analyze, and interpret spatial and temporal information about agricultural fields. Remote sensing (RS) provides cost-effective, large-scale, and repetitive observations of crop conditions, while recent advances in artificial intelligence, especially deep learning (DL), have significantly improved the accuracy of image classification and interpretation. Integrating RS with DL techniques has therefore become a key driver of innovation in agricultural monitoring and decision support. The aim of this study is to demonstrate how satellite data can be used to detect and map heterogeneity within fields and differences in crop growth, with particular emphasis on the application of advanced classification methods. In this study, I analyzed the spatial variability of intensively cultivated agricultural fields using several machine learning and deep learning-based algorithms (Cluster, k-means, SVM, k-NN). The results of the different classification methods were compared to identify the most effective approach for assessing crop development anomalies and supporting precision agriculture.