High-yield rice cultivation is an effective way to address the increasing food demand worldwide.Correct classification of high-yield rice is a key step of breeding.However,manual measurements within breeding programs ...High-yield rice cultivation is an effective way to address the increasing food demand worldwide.Correct classification of high-yield rice is a key step of breeding.However,manual measurements within breeding programs are time consuming and have high cost and low throughput,which limit the application in large-scale field phenotyping.In this study,we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders.In total,13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield.Using an Unmanned Aerial Vehicle(UAV)platform equipped with a hyperspectral camera to capture images over multiple time series,a rice yield classification model based on the XGBoost algorithm was proposed.Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not.The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice.Thus,we developed a low-cost,high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.展开更多
基金Agreement on Functional Gene-Mining and Selection of Superior Crop Performances to Lianfeng Gu,Digital Fujian Institute of Big Data for Agriculture and Forestry,Fujian Agriculture and Forestry University(KJG18019A08 to Bizhi Wu)Autonomous Region Key R&D Program(Special Talent Application 2018BEB04002)Innovation Team of Intelligence Assisted Phenotypic Analysis for Ningxia Crop,and Agricultural Breeding in Ningxia Hui Autonomous Region(2018NYYZ03 to Jian Wang).
文摘High-yield rice cultivation is an effective way to address the increasing food demand worldwide.Correct classification of high-yield rice is a key step of breeding.However,manual measurements within breeding programs are time consuming and have high cost and low throughput,which limit the application in large-scale field phenotyping.In this study,we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders.In total,13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield.Using an Unmanned Aerial Vehicle(UAV)platform equipped with a hyperspectral camera to capture images over multiple time series,a rice yield classification model based on the XGBoost algorithm was proposed.Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not.The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice.Thus,we developed a low-cost,high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.
