Automated detection of sugarcane crop lines from UAV images using deep learning  

作　　者：Joao Batista Ribeiro Renato Rodrigues da Silva Jocival Dantas Dias Mauricio Cunha Escarpinati Andre Ricardo Backes 

机构地区：[1]School of Computer Science-Federal University of Uberlandia,Uberlandia,MG,Brazil [2]Department of Computing-Federal University of Sao Carlos,Sao Carlos,SP,Brazil

出　　处：《Information Processing in Agriculture》2024年第3期385-396,共12页农业信息处理(英文)

基　　金：the financial support of CNPq(National Council for Scientific and Technological Development,Brazil)(Grant#307100/2021-9);the financial support of Fapemig(Fundacao deAmparo a Pesquisa do Estado de Minas Gerais);financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brazil(CAPES)-Finance Code 001.

摘　　要：UAVs(Unmanned Aerial Vehicles)have become increasingly popular in the agricultural sector,promoting and enabling the application of aerial image monitoring in both the scientific and business contexts.Images captured by UAVs are fundamental for precision farming practices.They enable us do a better crop planning,input estimates,early identification and correction of sowing failures,more efficient irrigation systems,among other tasks.Since all these activities deal with low or medium altitude images,automated identification of crop lines plays a crucial role improving these tasks.We address the problem of detecting and segmenting crop lines.We use a Convolutional Neural Network to segment the images,labeling their regions in crop lines or unplanted soil.We also evaluated three traditional semantic networks:U-Net,LinkNet,and PSPNet.We compared each network in four segmentation datasets provided by an expert.We also assessed whether the network’s output requires a post-processing step to improve the segmentation.Results demonstrate the efficiency and feasibility of these networks in the proposed task.

关 键 词：Crop line Aerial Images CNN UAV Precision agriculture 

分 类 号：S566[农业科学—作物学] TP391.41[自动化与计算机技术—计算机应用技术]