Toward Good Practices for Fine-Grained Maize Cultivar Identification With Filter-Specific Convolutional Activations

Crop cultivar identification is an important aspect in agricultural systems. Traditional solutions involve excessive human interventions, which is labor-intensive and timeconsuming. In addition, cultivar identification is a typical task of fine-grained visual categorization (FGVC). Compared with other common topics in FGVC, studies of this problem are somewhat lagging 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 by computer vision. In particular, a novel fine-grained maize cultivar identification data set termed HUST-FG-MCI that contains 5000 images is first constructed. To better capture the textual differences in a weakly supervised manner, we proposed an effective deep convolutional neural network and Fisher vector (FV)based feature encoding mechanism. The mechanism tends to highlight subtle object patterns via filter-specific convolutional representations and thus provides strong discrimination for cultivar identification. Experimental results demonstrate that our method outperforms other state-of-the-art approaches. We show also that FV encoding can weaken the linear dependency between convolutional activations, redundant filters exist in the convolutional layer, and high accuracy can be maintained with relatively low-dimensional convolutional features and one or two Gaussian components in FV.