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 …

Farmers have access to many data‐intensive technologies to help them monitor and control
weeds and pests. Data collection, data modelling and analysis, and data sharing have …

Accurate crop yield predictions are crucial for farmers and policymakers. Despite the
widespread use of ensemble machine learning (ML) models in computer science, their …

The main objectives of this study are (1) to compare several machine learning models to
predict county-level corn yield in the study area and (2) to compare the feasibility of machine …

Accurate and comprehensive modelling aimed at investigating the impact of climate change
on rainfed crop yields is of great importance due to the interconnected issues of water …

In an era marked by growing global population and climate variability, ensuring food security
has become a paramount concern. Rice, being a staple crop for billions of people, requires …

The crop yield prediction (CYP) has a high significance in agriculture. Early crop yield
predictions assist the farmers, decision-makers in making timely decisions during the actual …

This paper advocates the use of Semantically Enriched Computational Intelligence (SECI)
for managing the complex tasks of smart farming. Specifically, it proposes ontology-based …

A methodological framework has been proposed that consists of six different modules to
compute and analyze the dynamics of climate-related extremes on irrigation water …

With the advent of big data and the popularity of black-box deep learning methods, it is
imperative to address the robustness of neural networks to noise and outliers. We propose …