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Updated: Jan 9, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Published on: February 9, 2024

High-precision crop recommendation system with stacking ensemble classifiers for optimizing agricultural

Rania A Ahmed^1,2, Walid El-Shafai^3,4, Zeinab A Ahmed⁵

¹Climate Change Information Center and Renewable Energy and Expert System, Agricultural Research Center (ARC), Giza, Egypt. rania_abdelmordy@el-eng.menofia.edu.eg.

Scientific Reports

|December 8, 2025

Summary

This summary is machine-generated.

This study introduces an advanced crop recommendation system using feature fusion and ensemble models to boost crop yields. The novel approach significantly enhances accuracy and reduces overfitting for better agricultural decision-making.

Keywords:

Bagging Boosting Crop recommendation system Ensemble classifier ML Stacking

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Area of Science:

Agricultural Science
Machine Learning
Data Science

Background:

Crop productivity is vital for global food security and economic stability.
Yields are influenced by factors like climate, weather, and soil nutrient levels.
Effective crop recommendation systems are needed to optimize agricultural practices.

Purpose of the Study:

To develop an enhanced crop recommendation system using feature fusion and ensemble machine learning.
To improve the accuracy and reduce overfitting in crop yield prediction models.
To provide data-driven insights for farmers on optimal crop selection based on environmental factors.

Main Methods:

Implemented a stacking ensemble model with 18 classifiers.
Introduced three novel methods for feature fusion and overfitting mitigation.
Validated the model on two datasets, including one with 28,242 records.

Main Results:

Feature fusion improved accuracy and precision, outperforming existing techniques.
The proposed models demonstrated reduced overfitting, particularly on large datasets.
Ensemble models achieved accuracies ranging from 98.4% to 99.54% in crop categorization.
A voting ensemble classifier reached 99.56% accuracy on a small dataset.
A stacking ensemble classifier achieved 85.6% accuracy on a large dataset.

Conclusions:

Feature fusion is effective in enhancing the performance of ensemble crop recommendation systems.
The developed models offer a robust solution for improving crop productivity through data-driven recommendations.
The research highlights the potential of advanced machine learning techniques in precision agriculture.