Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach

TL;DR

This paper presents a deep learning-based crop recommendation system for Bangladesh that uses multivariate weather forecasting to improve agricultural decision-making and mitigate weather-related risks.

Contribution

It introduces a novel multivariate Stacked Bi-LSTM weather forecasting model integrated into a crop recommendation system tailored for Bangladesh.

Findings

Weather forecast model achieves an R-Squared of 0.9824.

System effectively predicts rainfall, temperature, humidity, and sunshine.

Farmers receive timely alerts and crop suggestions based on weather forecasts.

Abstract

Agriculture plays a fundamental role in driving economic growth and ensuring food security for populations around the world. Although labor-intensive agriculture has led to steady increases in food grain production in many developing countries, it is frequently challenged by adverse weather conditions, including heavy rainfall, low temperatures, and drought. These factors substantially hinder food production, posing significant risks to global food security. In order to have a profitable, sustainable, and farmer-friendly agricultural practice, this paper proposes a context-based crop recommendation system powered by a weather forecast model. For implementation purposes, we have considered the whole territory of Bangladesh. With extensive evaluation, the multivariate Stacked Bi-LSTM (three Bi-LSTM layers with a time Distributed layer) Network is employed as the weather forecasting model. The proposed weather model can forecast Rainfall, Temperature, Humidity, and Sunshine for any given location in Bangladesh with an average R-Squared value of 0.9824, and the model outperforms other state-of-the-art LSTM models. These predictions guide our system in generating viable farming decisions. Additionally, our full-fledged system is capable of alerting the farmers about extreme weather conditions so that preventive measures can be undertaken to protect the crops. Finally, the system is also adept at making knowledge-based crop suggestions for flood and drought-prone regions.