Bangladeshi Paddy Yield Estimation by Deep Learning Approach

Bangladesh is facing a severe risk as a result of climate change, as there are six seasons in Bangladesh, each containing its circumstantial behavior. Agriculture is the main arm of this country for economic growth as ${2 8 \%}$ of people are farmers. The majority of citizens rely on rice for daily sustenance, which makes paddy a more important crop. But storms, rain, and drought are the main barriers to crop production in this country. Every year thousands of tons of paddy losses to adverse weather. In the era of Artificial Intelligence, we can make decisions by historical data. In this study, we will predict the yield of three major crops Aus, Aman, and Boro. For this purpose, we used 5 years (2019-2023) of weather and yield data for these crops. 11 different features are utilized in this work by 3 different deep learning architectures: ANN-based, LSTM-based, and LSTM + Ridge Regression-based hybrid model. The highest r2 score 0.90 with a mean squared error of ${0. 1 2}$ was achieved by our hybrid model 3.