PeanutNeRF: 3D Radiance Field for Peanuts

Saeed, Farah; Sun, Jin; Ozias-Akins, Peggy; Chu, Ye Juliet; Li, Changying Charlie

doi:10.1109/cvprw59228.2023.00665

Cited by 1 publication

(1 citation statement)

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“…Depth cameras and lidar can directly obtain accurate 3D point cloud data of plants. However, this method has the disadvantages of high equipment cost, low acquisition efficiency, and difficulty in obtaining point cloud data from multiple perspectives at the same time ( Saeed et al 2023a ). In contrast, to meet the demand for refined data acquisition at the individual plant scale and organ scale, the 3D reconstruction based on multi-view images has been proven more effective.…”

Section: Introductionmentioning

confidence: 99%

Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat

Li,

Wu,

Wen

et al. 2024

AoB PLANTS

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It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.

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Section: Introductionmentioning

confidence: 99%