A genetic algorithm-assisted deep learning approach for crop yield prediction.

The world population continues to increase which imposes rising demand in agriculture production. How to improve crop breeding to feed the growing population is a significant challenge. The traditional crop breeding is resource-intensive and time-consuming. Predictive modeling on crop yield can speed up the breeding process and make it resource-efficient. In this paper, a genetic algorithm (GA)-assisted deep learning solution method is proposed for the crop yield prediction. The proposed method consists of two phases, i.e., the global search phase and the local search phase. In the global search phase, GA is used to search for the best initial weights of the neural network. In the local search phase, random perturbation is added to avoid the local optimum and vanishing gradient problems. A case study of crop yield prediction is conducted to compare the proposed method and other gradient-based methods. The results show that the proposed method outperforms the gradient-based methods in terms of convergence speed and prediction accuracy. [ABSTRACT FROM AUTHOR]