  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.
