Crop plant breeding involves selecting and developing new plant varieties with desirable
traits such as increased yield, improved disease resistance, and enhanced nutritional value …

Rice plays a vital role as the main food source for almost half of the global population,
contributing more than 21% of the total calories humans need. Production predictions are …

Accurate forecasting of crop yields holds paramount importance in guiding decision-making
processes related to breeding efforts. Despite significant advancements in crop yield …

Predicting crop yields is a critical issue in agricultural production optimization and
intensification research. Accurate foresights of natural circumstances a year in advance can …

Low levels of agricultural productivity are associated with the persistence of food insecurity,
poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics …

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting
models. However, it is still challenging to identify the most critical features from a dataset …

The growing global requirement for food and the need for sustainable farming in an era of a
changing climate and scarce resources have inspired substantial crop yield prediction …

A reliable and accurate forecasting model for crop yields is crucial for effective decision-
making in every agricultural sector. Machine learning approaches allow for building such …

In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield
prediction to decide their import-export policies. The present work aims to assess the impact …

Process-based agroecosystem models are powerful tools to assess performance of
managed landscapes, but their ability to accurately represent reality is limited by the types of …