首页|Study Data from Northwest A&F University Update Knowledge of Machine Learning (A Cooperative Regulation Method for Greenhouse Soil Moisture and Light Using Gaussian Curvature and Machine Learning Algorithms)
Study Data from Northwest A&F University Update Knowledge of Machine Learning (A Cooperative Regulation Method for Greenhouse Soil Moisture and Light Using Gaussian Curvature and Machine Learning Algorithms)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Shaanxi, People's Republic of China, by NewsRx correspondents, research stated, "Soil moisture (SM) exerts a significant impact on crop growth, interacting with environmental factors such as temperature, photosynthetic photon flux density (PPFD), and CO2, ultimately affecting crop photosynthesis (Pn). This study employs a nested experimental design to investigate the photosynthetic activity of cucumber seedlings under diverse environmental conditions and establishes a support vector regression (SVR) model for Pn prediction." Financial support for this research came from Shaanxi Provincial Key Research and Development Project on Research and Demonstration of Intelligent Management Information System for Agricultural Irrigation (CN).
ShaanxiPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningNorthwest A&F University
NETL
NSTL
Elsevier
