Técnicas de reamostragem e super-resolução em imagens de culturas agrícolas

Abstract

The increasing demand for food, coupled with climate change, has driven the development of agricultural monitoring technologies to increase the efficiency and sustainability of crop production such as sugarcane and corn. However, the low resolution of images captured by Unmanned Aerial Vehicle (UAV) and satellites limits the detailed analysis of essential agronomic features. This thesis investigates methods to improve the resolution of agricultural images, comparing Traditional Resampling Techniques (TRT) with Super-Resolution with Deep Networks (SRDN) algorithms, such as Real Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), Multi-Level upscaling Transform (MuLUT) and Learning Resampling Function (LeRF). The aim of this study is to investigate the application of deep learning techniques to improve the resolution of agricultural images. For this purpose, existing methods were reviewed and an agricultural dataset was prepared. The research adopted an experimental approach, evaluating the methods quantitatively using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and qualitatively by visual analysis. The experiments demonstrate significant improvements in image resolution using the SRDN algorithms compared to TRT, with gains of 484.34% in sugarcane images, 234.4% in corn, and 58.57% in satellite images. Although the SRDN techniques were developed for other purposes, such as improving the resolution of images of people and anime, their performance can be observed in agricultural images. The results obtained are significant for precision agriculture, since the increase in image resolution can aid in monitoring plant growth and health, providing faster and more effective interventions. In future investigations, we hope to expand the comparisons with other SRDN algorithms.