2023-03-06 ノースカロライナ州立大学(NCState)
この技術は、従来の手法に比べてデータ収集を効率的に行い、植物育種家に有用なデータをより迅速に提供することができる。角度を測定するために、ロボットの上部に複数の層のカメラを備えたタワーが取り付けられ、30インチの間隔で配置された作物の行を自動で測定する。
この角度測定装置は、トウモロコシなどの植物の光合成効率に影響を与えるため、植物育種研究者にとって重要である。
＜関連情報＞
- https://news.ncsu.edu/2023/03/leaf-measuring-robot/
- https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22166
ステレオビジョンと深層畳み込みニューラルネットワークを用いたトウモロコシ植物のフィールドベースロボットによる葉角検出と特性評価 Field-based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks
Lirong Xiang, Jingyao Gai, Yin Bao, Jianming Yu, Patrick S. Schnable, Lie Tang
Journal of Field Robotics Published: 27 February 2023
DOI:https://doi.org/10.1002/rob.22166
Abstract
Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three-dimensional (3D) models of field-grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side-view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high-speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two-dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Satisfactory accuracies in terms of correlation coefficient (r) and mean absolute error (MAE) were achieved for leaf angle ( γ＞0.87,MAE＜5。) and internode heights (γ＞0.99,MAE＜3.5㎝). Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture.
