Enhancing Corn Image Resolution Captured by Unmanned Aerial Vehicles With the Aid of Deep Learning - Emilia Alves Nogueira; Bruno Moraes Rocha; Gabriel da Silva Vieira; Afonso Ueslei da Fonseca; Juliana Paula Felix; Antonio Oliveira-Jr; Fabrizzio Soares (IEEE Access)
The increasing global population has heightened the demand for food, making it crucial to optimize agricultural practices. As a versatile crop, corn plays a crucial role in global food security, particularly in countries like the United States, Brazil, China, and Argentina, where corn farming is a major economic driver. To meet the growing demand and ensure sustainable practices, innovative technologies such as Unmanned Aerial Vehicles (UAVs) are being increasingly utilized in agriculture. UAVs provide efficient monitoring and sustainable methods, enabling farmers to gather precise data on crop health and yield potential. However, to capture high-resolution images that are essential for accurate analysis, UAVs often need to fly at lower altitudes. This can lead to challenges, such as crop movement caused by the wind generated by the UAV propellers, which can affect the quality of the data collected. On the other hand, images captured at high altitudes present challenges, such as blur and low resolution, which can compromise the extraction of essential characteristics of the crop under analysis. To overcome these limitations, the authors propose the use of some techniques to improve the resolution of the post-flight image of the corn crop. Among them are the classical interpolation techniques such as Nearest Neighbor, Bilinear and Bicubic, as well as Super Resolution (SR) algorithms based on deep learning, such as MuLUT, LeRF and Real-ESRGAN. The metrics used to evaluate the quality of the resulting images were Euclidean, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Jaccard and Learned Perceptual Image Patch Similarity (LPIPS). Experimental results demonstrate significant improvements in the resolution in the images using these SR algorithms compared to traditional interpolation methods, with gains of 364.13%. Although SR techniques have been developed for other purposes, such as improving the resolution of images of people and landscapes, their considerable performance can be observed in agricultural images. These techniques can aid in the early detection of pests, diseases, and nutritional deficiencies, facilitating faster and more effective interventions in corn crops. The results obtained from this study are significant for precision agriculture since increasing the resolution of images can improve the monitoring of plant growth and health, providing faster and more effective interventions. One point to be investigated is the adaptation of these techniques to different types of crops and environmental conditions, in order to expand their potential for applica- tion in different agricultural regions. In future investigations, we hope to refine the accuracy of the proposed approaches, as well as expand the comparisons with other super-resolution algorithms. In addition, tests will be carried out with different datasets, including satellite images, to evaluate the specificity in apply these techniques in different scenarios. This will allow a more comprehensive analysis of the impact of these solutions in the agricultural area.