首页|Study Data from State University of New York (SUNY) Buffalo Provide New Insights into Machine Learning (Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern . ..)
Study Data from State University of New York (SUNY) Buffalo Provide New Insights into Machine Learning (Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern . ..)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting from New York City , United States, by NewsRx journalists, research stated, “Knowledge of the facto rs influencing nutrient-limited subtropical maize yield and subsequent predictio n is crucial for effective nutrient management, maximizing profitability, ensuri ng food security, and promoting environmental sustainability. We analyzed data f rom nutrient omission plot trials (NOPTs) conducted in 324 farmers’ fields acros s ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.” Our news editors obtained a quote from the research from State University of New York (SUNY) Buffalo: “An additive main effect and multiplicative interaction (A MMI) model was used to explain maize yield variability with nutrient addition. I nterpretable machine learning (ML) algorithms in automatic machine learning (Aut oML) frameworks were subsequently used to predict attainable yield relative nutr ient-limited yield (RY) and to rank variables that control RY. The stack-ensembl e model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model’s square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature import ance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn.”
State University of New York (SUNY) Buff aloNew York CityUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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