{"title":"基于神经网络的两个水稻品种“Koshihikari”和“Takanari”冠层温差预测及其环境响应估计","authors":"Rintaro Kondo, Yu Tanaka, T. Shiraiwa","doi":"10.1080/1343943X.2022.2103003","DOIUrl":null,"url":null,"abstract":"ABSTRACT Canopy photosynthesis is an important component of biomass production in field-grown rice (Oryza sativa L.). Although canopy temperature differences (CTD) provide important information for evaluating canopy photosynthesis, the measurement of CTD is still a labor-intensive task. Therefore, we designed this study to establish a model for predicting CTD under different field conditions using meteorological data and evaluated the environmental response of CTD using the established model. Our study collected 2,056,264 CTD data points from two rice cultivars having different photosynthetic capacities, ‘Koshihikari’ and ‘Takanari’, and then used these data to create a novel model using a neural network (NN). The input variables were limited to meteorological data, and the output variable was set to CTD. The established NN model produced a prediction accuracy of R2 = 0.792 and RMSE = 0.605°C. We then used this NN model to simulate the CTD response of the Koshihikari and Takanari cultivars in response to various environmental changes. These predictions revealed that Takanari had a lower CTD than Koshihikari when exposed to high relative humidity (RH) or low to moderate solar radiation (Rs ). In contrast, the CTD of Koshihikari tended to be lower than that of Takanari under lower RH or higher Rs . This result implies that the advantages of the single-leaf gas exchange system in Takanari can be mitigated under extremely high-VPD conditions. Thus, our new method may provide a powerful tool to gain a better understanding of gas exchange, growth processes, and varietal differences in rice cultivated under field conditions. Graphical Abstract","PeriodicalId":20259,"journal":{"name":"Plant Production Science","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting rice (Oryza sativa L.) canopy temperature difference and estimating its environmental response in two rice cultivars, ‘Koshihikari’ and ‘Takanari’, based on a neural network\",\"authors\":\"Rintaro Kondo, Yu Tanaka, T. Shiraiwa\",\"doi\":\"10.1080/1343943X.2022.2103003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Canopy photosynthesis is an important component of biomass production in field-grown rice (Oryza sativa L.). Although canopy temperature differences (CTD) provide important information for evaluating canopy photosynthesis, the measurement of CTD is still a labor-intensive task. Therefore, we designed this study to establish a model for predicting CTD under different field conditions using meteorological data and evaluated the environmental response of CTD using the established model. Our study collected 2,056,264 CTD data points from two rice cultivars having different photosynthetic capacities, ‘Koshihikari’ and ‘Takanari’, and then used these data to create a novel model using a neural network (NN). The input variables were limited to meteorological data, and the output variable was set to CTD. The established NN model produced a prediction accuracy of R2 = 0.792 and RMSE = 0.605°C. We then used this NN model to simulate the CTD response of the Koshihikari and Takanari cultivars in response to various environmental changes. These predictions revealed that Takanari had a lower CTD than Koshihikari when exposed to high relative humidity (RH) or low to moderate solar radiation (Rs ). In contrast, the CTD of Koshihikari tended to be lower than that of Takanari under lower RH or higher Rs . This result implies that the advantages of the single-leaf gas exchange system in Takanari can be mitigated under extremely high-VPD conditions. Thus, our new method may provide a powerful tool to gain a better understanding of gas exchange, growth processes, and varietal differences in rice cultivated under field conditions. 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Predicting rice (Oryza sativa L.) canopy temperature difference and estimating its environmental response in two rice cultivars, ‘Koshihikari’ and ‘Takanari’, based on a neural network
ABSTRACT Canopy photosynthesis is an important component of biomass production in field-grown rice (Oryza sativa L.). Although canopy temperature differences (CTD) provide important information for evaluating canopy photosynthesis, the measurement of CTD is still a labor-intensive task. Therefore, we designed this study to establish a model for predicting CTD under different field conditions using meteorological data and evaluated the environmental response of CTD using the established model. Our study collected 2,056,264 CTD data points from two rice cultivars having different photosynthetic capacities, ‘Koshihikari’ and ‘Takanari’, and then used these data to create a novel model using a neural network (NN). The input variables were limited to meteorological data, and the output variable was set to CTD. The established NN model produced a prediction accuracy of R2 = 0.792 and RMSE = 0.605°C. We then used this NN model to simulate the CTD response of the Koshihikari and Takanari cultivars in response to various environmental changes. These predictions revealed that Takanari had a lower CTD than Koshihikari when exposed to high relative humidity (RH) or low to moderate solar radiation (Rs ). In contrast, the CTD of Koshihikari tended to be lower than that of Takanari under lower RH or higher Rs . This result implies that the advantages of the single-leaf gas exchange system in Takanari can be mitigated under extremely high-VPD conditions. Thus, our new method may provide a powerful tool to gain a better understanding of gas exchange, growth processes, and varietal differences in rice cultivated under field conditions. Graphical Abstract
期刊介绍:
Plant Production Science publishes original research reports on field crops and resource plants, their production and related subjects, covering a wide range of sciences; physiology, biotechnology, morphology, ecology, cropping system, production technology and post harvest management. Studies on plant production with special attention to resource management and the environment are also welcome. Field surveys on cropping or farming system are also accepted. Articles with a background in other research areas such as soil science, meteorology, biometry, product process and plant protection will be accepted as long as they are significantly related to plant production.