Paddy Yield Estimation By Deep Learning Approach

Abstract

Three crucial crops in Bangladesh are farmed concurrently based on the country's climate and seasons. The individuals mentioned are Aush, Aman, and Boro. Various sorts of damage occur in Bangladesh at different times as a result of natural catastrophes, in accordance with the country's climate. Examples include storms, torrential downpours, floods, and river overflows. They inflict harm. Rice is the primary agricultural product of Bangladesh. The rice yield is significantly impacted by these natural disasters. Consequently, a multitude of different natural disasters transpire. These encompass agricultural yield decline, insufficiency in food supply, and potentially even widespread starvation. In order to address these issues, I have devised a model that can accurately predict the rice yield for the current season by examining historical data. For this particular situation, I have employed D planning. Among the three models I have tested, the LSTM model demonstrated superior performance. The third model demonstrated an R2 square A score of 0.77% with less of loss at 0.22%. Here use of 1560 data. The model has achieved unprecedented advancements.