Soybean cultivar recognition plays a vital role in cultivar evaluation, selection and production. Recently, there is an increasing interest in taking leaf image patterns as clues for distinguishing soybean cultivars. However, due to the higher inter-class similarity of soybean cultivars over plant species, the cultivar classification accuracies reported by the existing methods are far lower than those published on plant species recognition which make computer vision community have a concern whether leaf image patterns can provide sufficient discriminative information for identifying soybean cultivars. In this paper, we explore fusing deep learning features of leaves from different parts of soybean plants for achieving an accurate cultivar recognition. In our method, the deep learning features of triplet leave image patterns that consists of leaves from the lower, middle, and upper parts of soybean plants are fused by two methods, distance fusion and classifier fusion. In the former, the L1 distance measurements defined on the deep feature spaces of triplet leaf image patterns are fused prior to using 1NN classifier for classification. While in the later, the SVM classifiers trained by the deep features of triple leaf image patterns are combined by sum rule for cultivar prediction. We use the SoyCultivar200 leaf dataset which consists of 6000 samples from 200 soybean cultivars as benchmark. Our method achieves an exciting classification rate of 83.55% which demonstrates that our proposed fusion of deep features of triplet leaf image patterns can provide strong discriminative information for accurately identifying soybean cultivars.