A novel genotype-by-environmental interaction machine-learning model can predict crop yield with environmental data and genetic information more efficiently and accurately than an established model.

Instead, for their study, the researchers chose two different machine learning algorithms, an artificial neural network and gene-expression programming. They first trained these algorithms on a ...

“In collaboration with Professor Taylor, who has expertise in machine learning and optimization, we established a prediction model that employs a trained algorithm that can estimate microplastic ...

Global solar radiation (Hg) is a foundational input for calculating evapotranspiration, crop growth, irrigation needs, and ...

A team of researchers has turned the keen eye of AI toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.

Ensemble models, comprising multiple algorithms or simulations to improve prediction accuracy, are increasingly being applied ...

A new machine-learning model for predicting crop yield using environmental data and genetic information can be used to develop new, higher-performing crop varieties.