High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner

Huang, Xia; Zheng, Shunyi; Zhu, Na

doi:10.3390/rs14020431

Cited by 10 publications

(2 citation statements)

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“…Although the Poisson surface [35] reconstruction method is widely used owing to establishing triangles in the low-density region, the fused point cloud is insufficient to reconstruct a complete 3D model of the object by such a method. Thus, this article adopts the α-shape threedimensional surface reconstruction algorithm [36,37].…”

Section: Multi-angle Point-cloud Fusion and Surface Reconstructionmentioning

confidence: 99%

Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-Bag

Guo,

Mao,

Dai

et al. 2023

Remote Sensing

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Contactless and non-destructive measuring tools can facilitate the moisture monitoring of bagged or bulk grain during transportation and storage. However, accurate target recognition and size prediction always impede the effectiveness of contactless monitoring in actual use. This paper developed a novel 3D reconstruction method upon multi-angle point clouds using a binocular depth camera and a proper Yolo-based neural model to resolve the problem. With this method, this paper developed an embedded and low-cost monitoring system for the in-warehouse grain bags, which predicted targets’ 3D size and boosted contactless grain moisture measuring. Identifying and extracting the object of interest from the complex background was challenging in size prediction of the grain silo-bag on a conveyor. This study first evaluated a series of Yolo-based neural network models and explored the most appropriate neural network structure for accurately extracting the grain bag. In point-cloud processing, this study constructed a rotation matrix to fuse multi-angle point clouds to generate a complete one. This study deployed all the above methods on a Raspberry Pi-embedded board to perform the grain bag’s 3D reconstruction and size prediction. For experimental validation, this study built the 3D reconstruction platform and tested grain bags’ reconstruction performance. First, this study determined the appropriate positions (−60°, 0°, 60°) with the least positions and high reconstruction quality. Then, this study validated the efficacy of the embedded system by evaluating its speed and accuracy and comparing it to the original Torch model. Results demonstrated that the NCNN-accelerated model significantly enhanced the average processing speed, nearly 30 times faster than the Torch model. The proposed system predicted the objects’ length, width, and height, achieving accuracies of 97.76%, 97.02%, and 96.81%, respectively. The maximum residual value was less than 9 mm. And all the root mean square errors were less than 7 mm. In the future, the system will mount three depth cameras for achieving real-time size prediction and introduce a contactless measuring tool to finalize grain moisture detection.

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Section: Multi-angle Point-cloud Fusion and Surface Reconstructionmentioning

confidence: 99%

Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-Bag

Guo,

Mao,

Dai

et al. 2023

Remote Sensing

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“…Whereas HTPP platforms used for outdoor conditions frequently include ground vehicles and UAVs [2]. Handheld setups are used both for indoor and outdoor conditions [18][19][20]. The selection of the platform is also guided by the desired spatial and temporal resolution, and the scale and scope of the experiments to be performed.…”

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confidence: 99%

Development of a Quick-Install Rapid Phenotyping System

Buelvas

Adamchuk

Lan

et al. 2023

Sensors

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In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

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