A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images  

作　　者：Ni Jiang Wanneng Yang Lingfeng Duan Guoxing Chen Wei Fang Lizhong Xiong Qian Liu 

机构地区：[1]Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology 1037 Luoyu Rd.,Wuhan 430074,P.R.China [2]National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research Huazhong Agricultural University Wuhan 430070,P.R.China [3]College of Engineering Huazhong Agricultural University Wuhan 430070,P.R.China [4]MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River College of Plant Science and Technology Huazhong Agricultural University Wuhan 430070,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2015年第2期7-18,共12页创新光学健康科学杂志（英文）

基　　金：supported by grants from the National Program on High Technology Development (2013AA102403);the National Program for Basic Research of China (2012CB114305);the National Natural Science Foundation of China (30921091,31200274);the Program for New Century Excellent Talents in University (No.NCET-10-0386);the Fundamental Research Funds for the Central Universities (No.2013PY034).

摘　　要：Total green leaf area(GLA)is an important trait for agronomic studies.However,existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive.A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented.Using projected areas of the plant in images,linear,quadratic,exponential and power regression models for estimating total GLA were evaluated.Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area.And power models fit better than other models.In addition,the use of multiple side-view images was an efficient method for reducing the estimation error.The inclusion of the top-view projected area as a seoond predictor provided only a slight improvement of the total leaf area est imation.When the projected areas from multi angle images were used,the estimated leaf area(ELA)using the power model and the actual leaf area had a high correlation cofficient(R2>0.98),and the mean absolute percentage error(MAPE)was about 6%.The method was capable of estimating the total leaf area in a nondestructive,accurate and eficient manner,and it may be used for monitoring rice plant growth.

关 键 词：Agri photonics image processing plant phenotyping regression model visible light imaging 

分 类 号：S51[农业科学—作物学]