Rice Cultivation Planning Using A Deep Learning Neural Network

This paper represents an application of a Deep Qlearning in the rice crop cultivation practice, where the optimal actions are determined. In this work, we focused on rain-fed rice where the main actions are when to start cultivation and when to harvest. The goal is to find the optimum cultivation and harvest period such that farmers’ income is maximized. To make our study realistic, the actual climatic data from each district in Thailand together with the actual rice yields were used to construct the yield estimator where the decision tree with Adaboost was employed. Next, we constructed the simulated farming environments for different districts in Thailand and employed the deep Q-learning neural network to find the optimum actions. From our simulated examples, the deep Q-learning may allow farmers to earn more than the average income.