One of the primary challenges in implementing intelligent systems in agriculture is safeguarding the privacy and security of ...

To quickly sort through such a huge data set, they've turned to machine learning, a way of using computers to identify patterns and make predictions based on the information. The group's results ...

New research from The University of Texas at Arlington and the U.S. Department of Agriculture demonstrates how mathematical ...

This project uses machine learning techniques ... for real-time yield predictions based on user-inputted farming conditions. The trained LightGBM model is deployed using Gradio. Users can input ...

This project focuses on utilizing the power of machine learning to forecast crop yields in India ... enabling the creation of an accurate and reliable model for crop yield prediction. This innovative ...

This chapter attempts to improve the accuracy and efficiency of applying machine learning algorithms for predicting ... selected dataset of important agricultural parameters. The “crop yield ...

Colombia’s agriculture, central to food security and economic resilience, is marked by significant heterogeneity across its ...

Many of these dynamics play out during the procurement phase, before ground is even broken on a new project. Francesco Decarolis is a Full Professor of Economics at Bocconi University, and mostly ...