Abstract Machine learning is an important decision support tool for crop yield prediction,
including supporting decisions on what crops to grow and what to do during the growing …

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …

Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …

Investment in technological research is imperative to stimulate the development of
sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and …

The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …

Recent expansion of croplands in the United States has caused widespread conversion of
grasslands and other ecosystems with largely unknown consequences for agricultural …

Despite the wide adoption of emergency remote learning (ERL) in higher education during
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …

Crop yield prediction is extremely challenging due to its dependence on multiple factors
such as crop genotype, environmental factors, management practices, and their interactions …

Many studies have applied machine learning to crop yield prediction with a focus on specific
case studies. The data and methods they used may not be transferable to other crops and …