The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

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 use of satellites to monitor crops and support their management is gathering increasing
attention. The improved temporal, spatial, and spectral resolution of the European Space …

Smart Farming is the new term in the agriculture sector, aiming to transform the traditional
techniques to innovative solutions based on Information Communication Technologies …

Abstract The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which
may be used for acquiring solutions towards better crop management. The applications of …

Agricultural management at field-scale is critical for improving yield to address global food
security, as providing enough food for the world's growing population has become a wicked …

Large-scale crop yield estimation is, in part, made possible due to the availability of remote
sensing data allowing for the continuous monitoring of crops throughout their growth cycle …

Accurate prediction of crop yield at the field scale is critical to addressing crop production
challenges and reducing the impacts of climate variability and change. Recently released …

Knowing the expected crop yield in the current growing season provides valuable
information for farmers, policy makers, and food processing plants. One of the main benefits …

The data generated in modern agricultural operations are provided by diverse elements,
which allow a better understanding of the dynamic conditions of the crop, soil and climate …