There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …

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 …

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 …

Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Developing similar techniques for satellite …

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 …

Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

Preharvest crop yield prediction is critical for grain policy making and food security. Early
estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …