摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者从阿伯米-卡拉维大学传来的消息，研究表明，“谷物对生物和非生物因素复杂组合中的微小变化敏感。这种复杂性可以通过机器学习(ML)等技术来解读。”我们的新闻编辑引用了Abomey-Ca Lavi大学的研究：“使用PRISMA方法，”本文以2007~2023年6个数据库的115篇文章为基础,探讨了谷物产量预测的特点和ML技术。结果表明:所研究的谷物产量预测数据大多来自次酸CES,仅有28.68%采用试验或原始数据,中国(31)和美国(18)贡献最大,小麦(48%)、玉米(33%)和水稻(17%)是研究最多的谷物。遥感数据和土壤参数是最常用的预测因子。最常用的ML技术是支持向量机(SVM)(51%)、多层感知器(MLP)(41%)、线性回归(34%)、随机森林(RF)(24%)和XGBoo ST(20%)。然而，基于已报道的R-平方和平均绝对误差(MAE)，RF、MLP和SVM模型是预测谷物产量的最佳技术。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from the University of Abomey-Calavi by NewsRx correspondents, research stated, “Cereals are sensit ive to small changes in complex combinations of biotic and abiotic factors. Such a complexity can be deciphered using techniques such as Machine learning (ML).” Our news editors obtained a quote from the research from University of Abomey-Ca lavi: “Using the PRISMA approach, this paper explores the features and ML techni ques in cereal yield prediction based on 115 articles from 2007 to 2023 in six d atabases. Results showed that most data in the articles were from secondary sour ces and only 28.68% used experiments or primary data. China (31) a nd the United States (18) contributed most. Wheat (48%), maize (33% ), and rice (17%) represented the most studied cereals. Climate, re mote sensing data, and soil parameters were the most used predictors. The most f requently used ML techniques for cereal prediction were support vector machine ( SVM) (51%), multilayer perceptron (MLP) (41%), linear regression (34%), random forest (RF) (24%), and XGBoo st (20%). However, RF, MLP, and SVM models were the best-performing techniques to predict grain yield based on reported R-square and mean absolute error (MAE).”
NETL
NSTL