Development of a simple prediction model for cowpea yield under environmentally growth-restricted conditions.

New and simple crop yield prediction methods are expected to be developed owing to the increasing environmental stress caused by climate change. Algorithms of machine learning could be a powerful tool for predicting crop yield; however, the required feature variables and differences in their prediction accuracy are poorly addressed. The objectives of this study were to identify the best combination of feature variables to predict the yield of cowpea (Vigna unguiculata), which is widely grown in central Sudan Savanna under environmentally restricted conditions, and clarify the differences in the accuracy of major machine learning algorithms.