Fine-grained maize cultivar identification using filter-specific convolutional activations

Abstract

Cultivar identification is an important aspect in agriculture and also a typical task of fine-grained visual categorization (FGVC). In comparison with other common topics in FGVC, studies on this problem are somewhat lagged and limited. In this paper, targeting four Chinese maize cultivars of Jundan No.20, Wuyue No.3, Nongda No.108, and Zhengdan No.958, we first consider the problem of identifying the maize cultivar based on its tassel characteristics. Technically, an effective convolutional neural network (CNN) based feature encoding pipeline that allows integration of deep CNN based column feature extraction, filter-specific Fisher vector (FV) encoding and mutual information (MI) based filter selection is proposed to better address this problem. In particular, a novel fine-grained maize cultivar identification dataset termed MCI-4000 that contains 4000 images is first constructed by our team. Experimental results demonstrate that our method outperforms other stat-of-the-art approaches by at least 5% in accuracy. We also show that, there exists redundant filters in the last convolutional layer, and high accuracy can be achieved with only relatively low-dimensional column features and a small number of Gaussian components in FV.