首页|Study Findings from University of Abomey-Calavi Provide New Insights into Machin e Learning (Machine Learning Techniques for Cereal Crops Yield Prediction: A Com prehensive Review)
Study Findings from University of Abomey-Calavi Provide New Insights into Machin e Learning (Machine Learning Techniques for Cereal Crops Yield Prediction: A Com prehensive Review)
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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).”
University of Abomey-CalaviCyborgsEm erging TechnologiesMachine Learning
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