Enhancing Paddy Production Forecasts with Artificial Neural Networks Models and Weather Data

M. Arjun, Eshwar Guntuka, M. Vara Prasad, Madhavi Devi B.

2025

Abstract

Rice yield have a major role in ensuring no shortage of food, especially in countries like India, where a significant portion of the population relies on rice as a staple food. However, rice yield is highly sensitive to climatic variations, making accurate predictions essential to have effective- planning and making the decisions. This paper presents an Artificial-Neural-Network (ANN) based model to predict rice crop yields, using climatic data such as area, temperature (minimum, average, maximum), and Rainfall-of particular area. The study focuses on 34 districts of Maharashtra, India, covering the Kharif season (between June and November) for the years 1998 to 2019. The data was processed using the python libraries like numpy and pandas’ tool, and a Multilayer Perceptron ANN model was developed with a feedforward training algorithm. The ANN model demonstrated high prediction accuracy, achieving 97.54%, with sensitivity and specificity of 96.33% and 98.12%, respectively. Additionally, performance metrics such as Mean-Absolute-Error (MAE) (0.0526) and Root-Mean-Squared (RMS) Error (0.1527) indicate the robustness of the model. Compared to traditional regression-based models, the ANN approach provided more accurate yield forecasts by effectively capturing the nonlinear relationships between climatic variables and crop yield. This research contributes to the ongoing efforts to enhance agricultural forecasting using AI techniques and offers a tool that can assist farmers and policymakers in making informed decisions under variable climatic conditions. Future work aims to expand the model by incorporating additional factors like soil quality and pest infestations, as well as real-time data for dynamic predictions.

Paper Citation

in Harvard Style

Arjun M., Guntuka E., Prasad M. and B. M. (2025). Enhancing Paddy Production Forecasts with Artificial Neural Networks Models and Weather Data. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 731-734. DOI: 10.5220/0013889100004919

in Bibtex Style

@conference{icrdicct`2525,
author={M. Arjun and Eshwar Guntuka and M. Prasad and Madhavi B.},
title={Enhancing Paddy Production Forecasts with Artificial Neural Networks Models and Weather Data},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={731-734},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013889100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Enhancing Paddy Production Forecasts with Artificial Neural Networks Models and Weather Data
SN - 978-989-758-777-1
AU - Arjun M.
AU - Guntuka E.
AU - Prasad M.
AU - B. M.
PY - 2025
SP - 731
EP - 734
DO - 10.5220/0013889100004919
PB - SciTePress