首页|Findings from College of Agriculture Has Provided New Data on Machine Learning ( Decoding Potato Power: a Global Forecast of Production With Machine Learning and State-of-the-art Techniques)
Findings from College of Agriculture Has Provided New Data on Machine Learning ( Decoding Potato Power: a Global Forecast of Production With Machine Learning and State-of-the-art Techniques)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Jabalpur, India, by News Rx journalists, research stated, "As the second largest potato producer globally , reliable forecasts of output for India and major growing states are crucial. T his study developed autoregressive integrated moving average (ARIMA) models alon gside state space and gradient boosting machine learning techniques for annual p otato production spanning 1967-2020." The news correspondents obtained a quote from the research from the College of A griculture, "Model adequacy was evaluated using information criteria, errors met rics and out-of-sample validation. The chosen models provide the following forec asts: India is predicted to produce around 46,712 thousand metric tons, Uttar Pr adesh 13,900 thousand metric tons, West Bengal 11,544 thousand metric tons, Biha r 7710 thousand metric tons, Madhya Pradesh 3478 thousand metric tons, Gujarat 3 621 thousand metric tons and Punjab 2870 thousand metric tons over the period 20 21-2027. While no consistent superior approach emerged, tailoring models to capt ure data complexity and patterns for each state proved essential for generalizat ion." According to the news reporters, the research concluded: "Quantitatively assessi ng linearity, stationarity and outliers during model specification is key for st akeholders and policymakers needing precise predictions." This research has been peer-reviewed.
JabalpurIndiaAsiaCyborgsEmerging TechnologiesMachine LearningCollege of Agriculture
NETL
NSTL
