Precision agriculture, utilizing various information to manage crop production, has become the important approach to imitate the food supply problem around the world. Accurate prediction of crop yield is the main task of precision agriculture. With the help of neural networks, precision agriculture has progressed rapidly in past decades. However, neural networks are notoriously data-hungry anddata collection in agriculture is expensive and time-consuming. Bayesian neural network, extending the neural network with Bayes inference, is useful under such circumstance. Moreover, Bayesian allows to estimate uncertainty associated with prediction which makes the result more reliable. In this paper, a Bayesian neural network was applied a small dataset and the result shows Bayesian neural networkis more reliable under such circumstance.