Towards fine-grained maize tassel flowering status recognition: Dataset, theory and practice

Graphical abstractDisplay Omitted HighlightsWe first address the problem of flowering status recognition with computer vision.Densely sampled SIFT and Fisher vector are employed for feature representation.An effective metric learning method is proposed to leverage the performance.A maize tassel flowering status dataset of 3000 images is established. Maize is one of the three main cereal crops of the world. Accurately knowing its tassel flowering status can help to analyze the growth status and adjust the farming operation accordingly. At the current stage, acquiring the tassel flowering status mainly depends on human observation. Actually, it is costly and subjective, especially for the large-scale quantitative analysis under the in-field environment. To alleviate this, we propose an automatic maize tassel flowering status (i.e., non-flowering, partially-flowering and fully-flowering) recognition method via the computer vision technology in this paper. In particular, this task is formulated as a fine-grained image categorization problem. More specifically, scale-invariant feature transform (SIFT) is first extracted as the low-level visual descriptor to characterize the maize flower. Fisher vector (FV) is then applied to execute feature encoding on SIFT to generate more discriminative flowering status representation. To further leverage the performance, a novel metric leaning method termed large-margin dimensionality reduction (LMDR) is proposed. To verify the effectiveness of the proposed method, a flowering status dataset that consists of 3000 images is built. The experimental results demonstrate that our approach goes beyond the state-of-the-art by large margins (at least 8.3%). The dataset and source code are made available online.