首页|New Findings Reported from University of Minnesota Describe Advances in Machine Learning (Developing a New Active Canopy Sensor- and Machine Learning-based In-s eason Rice Nitrogen Status Diagnosis and Recommendation Strategy)
New Findings Reported from University of Minnesota Describe Advances in Machine Learning (Developing a New Active Canopy Sensor- and Machine Learning-based In-s eason Rice Nitrogen Status Diagnosis and Recommendation Strategy)
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Research findings on Machine Learning are discussed in a new report. According to news reporting originating in St. Pa ul, Minnesota, by NewsRx journalists, research stated, "Traditional critical nit rogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determi nation has limitations for in-season crop N diagnosis and recommendation under d iverse on-farm conditions. This study was conducted to (i) develop a new rice (O ryza sativa L.) critical N concentration (Nc) determination approach using veget ation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Ncdetermination approach and evaluate its reliability and practicality." Financial supporters for this research include Norwegian Ministry of Foreign Aff airs (SINOGRAIN III), Science and Technology Planning Project of Lhasa, United S tates Department of Agriculture (USDA).
St. PaulMinnesotaUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningNitroge nUniversity of Minnesota
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