Abstract:
Agriculture is the most important industry that influence the economy in Bangladesh. After freedom, the farming area was Bangladesh's really monetary main impetus. As Bangladesh is an agricultural country, the economy as well as food security of this country mostly depends on production level of different crops over the year. Crop yields are basically reliant on climate. The weather has a significant influence on crop output.AI can blast the farming field by changing the pay situation by developing the ideal harvest. This paper centers around anticipating the yield of the harvest by applying different machine-learning strategies. The expectation made by AI calculations will predict to the government and farmers how much crop yield in next year, by considering factors like temperature, area, rainfall, humidity and so on. Using data on crop yield from the ‘Bangladesh Agricultural Research Institute’. The most popular features, based on my data are area, rainfall, humidity, temperature and use of the algorithm is Arima, Long-Short-term Memory (LSTM), and Exponential Smoothing for time series forecasting. Also used classifier model for prediction production based on area, rainfall, humidity and temperature where used linear model and those are Linear Regression, Lasso Regression, Random Forest Regression and Decision Tree. From those algorithm Decision Tree provides maximum accuracy.
