Applying Machine Learning Approach To Predict Annual Yield of Major Crops and Recommend Planting Different Crops in Different Seasons in Bangladesh

Abstract

The Bangladesh economy heavily depends on agriculture. Bangladesh's agricultural sector is crucial for providing jobs, income, and GDP. Considering how dramatically the human population is growing, crop output is the primary factor in determining food security. In this study machine learning is used to predict Annual yield of major crops and recommend planting different crops in different seasons which are mostly cultivated all over Bangladesh. For getting the best accuracy, this study uses Decision tree, Random forest (RF), Support Vector Machine (SVM), Adaboost Classifier (ADB), KNN, Logistic regression (LOR), and the Naive Bayes (NB) algorithm. Algorithms for machine learning are used to analyze four most planted yields in Bangladesh. Those crops include: Rice (Aman, Aus, Boro), Potato, jute and wheat.