Naïve Bayes and Random Forest for Crop Yield Prediction

Abbas Maazallahi, Sreehari Thota, Naga Prasad Kondaboina, Vineetha Muktineni, Deepthi Annem +6 more
4/23/2024
cs.LGcs.AI

No confident code implementation found for this paper. 8 possible matches can be shown below.

Abstract

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, and Random Forest. The models, particularly Naïve Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 8 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data

87% match2024

A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors

87% match2024

Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review

85% match2023

From Rows to Yields: How Foundation Models for Tabular Data Simplify Crop Yield Prediction

82% match2025

UniCrop: A Universal, Multi-Source Data Engineering Pipeline for Scalable Crop Yield Prediction

81% match1 repo142026

A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting

81% match2025

Cite this paper

@article{maazallahi2024nave,
  title  = {Naïve Bayes and Random Forest for Crop Yield Prediction},
  author = {Abbas Maazallahi and Sreehari Thota and Naga Prasad Kondaboina and Vineetha Muktineni and Deepthi Annem and Abhi Stephen Rokkam and Mohammad Hossein Amini and Mohammad Amir Salari and Payam Norouzzadeh and Eli Snir and Bahareh Rahmani},
  year   = {2024},
  eprint = {2404.15392},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.15392v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.15392.svg)](https://codeofpaper.com/paper/2404.15392)

Discussion