High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks  

作　　者：Liang Liu Hao Lu Yanan Li Zhiguo Cao 

机构地区：[1]National Key Laboratory of Science and Technology on Multi-Spectral Information Processing,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan,430074 Hubei,China [2]The University of Adelaide,Adelaide,SA 5005,Australia [3]School of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan,430205 Hubei,China

出　　处：《Plant Phenomics》2020年第1期255-268,共14页植物表型组学（英文）

基　　金：Part of LL’s contribution was made when visiting the University of Adelaide.This work was supported in part by the Natural Science Foundation of China under Grant Nos.61876211 and 61906139 and in part by the Hubei Provincial Natural Science Foundation of China under Grant 2019CFB173.

摘　　要：Rice density is closely related to yield estimation,growth diagnosis,cultivated area statistics,and management and damage evaluation.Currently,rice density estimation heavily relies on manual sampling and counting,which is inefficient and inaccurate.With the prevalence of digital imagery,computer vision(CV)technology emerges as a promising alternative to automate this task.However,challenges of an in-field environment,such as illumination,scale,and appearance variations,render gaps for deploying CV methods.To fill these gaps towards accurate rice density estimation,we propose a deep learningbased approach called the Scale-Fusion Counting Classification Network(SFC^(2)Net)that integrates several state-of-the-art computer vision ideas.In particular,SFC^(2)Net addresses appearance and illumination changes by employing a multicolumn pretrained network and multilayer feature fusion to enhance feature representation.To ameliorate sample imbalance engendered by scale,SFC^(2)Net follows a recent blockwise classification idea.We validate SFC^(2)Net on a new rice plant counting(RPC)dataset collected from two field sites in China from 2010 to 2013.Experimental results show that SFC^(2)Net achieves highly accurate counting performance on the RPC dataset with a mean absolute error(MAE)of 25.51,a root mean square error(MSE)of 38.06,a relative MAE of 3.82%,and a R^(2) of 0.98,which exhibits a relative improvement of 48.2%w.r.t.MAE over the conventional counting approach CSRNet.Further,SFC^(2)Net provides high-throughput processing capability,with 16.7 frames per second on 1024×1024 images.Our results suggest that manual rice counting can be safely replaced by SFC^(2)Net at early growth stages.Code and models are available online at https://git.io/sfc2net.

关 键 词：ILLUMINATION ESTIMATION RENDER 

分 类 号：S511[农业科学—作物学]