Maize tassel area dynamic monitoring based on near-ground and UAV RGB images by U-Net model

Abstract

The dynamics of maize tassel area reflect the growth and development of maize plants, monitoring which facilitates crop breeding and management. At present, the monitoring of maize tassels mainly depends on manual work, which is very labor intensive and may be biased by human errors. The U-Net model has proved effective for crop segmentation using RGB imagery. However, there has not been a systematic study to test how the accuracy of U-Net model vary when applied to different maize varieties, at different tasseling stages, and on images of different spatial resolutions. Moreover, the capability of U-Net model for monitoring the dynamics of tassel area has not been explored. In this study, the potential of the U-Net model to provide an accurate segmentation of the tassels in complex situations from near-ground RGB images and UAV images were comprehensively studied. The results showed that the segmentation accuracy of U-Net model with Vgg16 as feature extraction network (IoU = 0.71) for tassels at the whole tasseling stages was better than that of U-Net model with MobileNet (IoU = 0.63). The U-Net model with Vgg16 as the feature extraction network maintained a good segmentation accuracy for maize tassels at different tasseling stages (IoU = 0.63–0.76), for different varieties (IoU = 0.65–0.79), and at different resolutions (IoU = 0.57–0.71), which proved the robustness of the model. Changes in the segmented area of tassels from images were basically consistent with the trends observed in the actual area of tassel measured manually. UAV RGB images with resolution of 3.06 mm showed a good segmentation accuracy (IoU = 0.54). In summary, the results showed that the U-Net model has a good segmentation accuracy of maize tassels under various complex situations. This study provides an effective method to monitor the maize tassel status in crop phenotyping experiments in the future.

Plant phenotyping relevance

トウモロコシ雄穂面積という植物形質を、近接RGB画像およびUAV画像からU-Netで抽出・推定する手法を開発し、品種・生育段階・解像度間で精度と頑健性を検証しており、フェノタイピング手法が中心である。

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