The field experiment designs with single replication were frequently used for factorial experiments in which the numbers of field plots were limited, but the experimental error was difficult to be estimated. To study... The field experiment designs with single replication were frequently used for factorial experiments in which the numbers of field plots were limited, but the experimental error was difficult to be estimated. To study a new statistical method for improving precision of regression analysis of such experiments in rice, 84 fertilizer experiments were conducted in 15 provinces of China, including Zhejiang, Jiangsu, Anhui, Hunan, Sichuan, Heilongjiang, etc. Three factors with 14 treatments (N: 0—225kg/ha, P: 0 —112. 5kg/ha, K: 0—150kg/ha) and two replications were employed using approaching optimun design. There were 2352 (84×14×2=2352) Yield deviations (d) between the individual treatment yields and its arithmetic mean. The results indicated that:展开更多
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.展开更多
文摘 The field experiment designs with single replication were frequently used for factorial experiments in which the numbers of field plots were limited, but the experimental error was difficult to be estimated. To study a new statistical method for improving precision of regression analysis of such experiments in rice, 84 fertilizer experiments were conducted in 15 provinces of China, including Zhejiang, Jiangsu, Anhui, Hunan, Sichuan, Heilongjiang, etc. Three factors with 14 treatments (N: 0—225kg/ha, P: 0 —112. 5kg/ha, K: 0—150kg/ha) and two replications were employed using approaching optimun design. There were 2352 (84×14×2=2352) Yield deviations (d) between the individual treatment yields and its arithmetic mean. The results indicated that:
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
