Fine-grained maize tassel trait characterization with multi-view representations

Abstract

The characteristics of maize tassel trait are important cues to improve the farming operation for production enhancement. Currently, the information obtained from the maize tassel mainly depends on human labor, which is subjective and labor-intensive. Recent researches have introduced several image-based approaches to overcome the shortage with a modest degree of success. However, due to the variation of cultivar, pose and illumination, and the clustered background, characterizing the maize tassel trait with computer vision remains a challenging problem. To this end, an automatic fine-grained machine vision system termed mTASSEL is developed in this paper. We proposed to characterize the maize tassel with multi-view representations that combine multiple feature views and different channel views, which can alleviate the influence of environmental variations. In addition to the total tassel number trait, some fine-grained tassel traits, including the tassel color, branch number, length, width, perimeter and diameter, are further characterized to execute the time-series monitoring. To boost the related research, a relatively large-scale maize tassel dataset (10 sequences with 16,031 samples) is first constructed by our team. The experimental results demonstrate that both system modules significantly outperform other state-of-the-art approaches by large margins (26.0% for the detection and 7.8% for the segmentation). Results of this research can serve the automatic growth stage detection, accurate yield estimation and machine detasseling, as well as the field-based phenotyping research. The dataset and source code of the system are available online.