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 …

Over the last few years, Deep learning (DL) approaches have been shown to outperform
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …

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 …

Accurate predictions of wheat yields are essential to farmers' production plans and to the
international trade in wheat. However, only poor approximations of the productivity of wheat …

Crop yield prediction is an important aspect of agriculture. The timely and accurate crop
yield predictions can be of great help for policy makers and farmers in planning and decision …

Timely and accurate crop yield estimation is important for adjusting agronomic management
and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new …

One important aspect of agriculture is crop yield prediction. This aspect allows decision-
makers and farmers to make adequate planning and policies. Before now, various statistical …

Farm-scale crop yield prediction is a natural development of sustainable agriculture,
producing a rich amount of food without depleting and polluting environmental resources …

This study addresses the challenge of accurate crop detection using satellite data, focusing
on the application of Long Short-Term Memory (LSTM) networks. The research employs a …

Data pre-processing is a technique that transforms the raw data into a useful format for
applying machine learning (ML) techniques. Feature selection (FS) and feature extraction …