Automated flower counting from partial detections: Multiple hypothesis tracking with a connected-flower plant model

Houtman, Wouter; Siagkris-Lekkos, Alexis; Bos, Dennis; Heuvel, B. J. P. van den; Boër, Michel; Elfring, Jos; Molengraft, M.J.G. van de

doi:10.1016/j.compag.2021.106346

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The findings revealed a counting accuracy of 95.56% for the 90 samples. By comparison, Houtman et al [30] reported a maximum counting accuracy of 92% for 71 Phalaenopsis plants, allowing for a margin of one flower. To be more precise, the actual accuracy was 61%.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, most of the aforementioned deep learning methods were basically limited to a fixed single viewpoint, and occlusions and overlaps were not fully considered, which is not conducive to achieving accurate flower counting. To address this problem, Houtman et al [30] proposed a nondeep learning method based on multiple hypothesis tracking (MHT) to count potted Phalaenopsis flowers. This method introduced multiple viewpoints and reached a maximum counting accuracy of 92% with a margin of one flower.…”

Section: Introductionmentioning

confidence: 99%

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Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

Yang,

Zhang,

et al. 2024

Agronomy

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The accurate detection and counting of flowers ensure the grading quality of the ornamental plants. In automated potted flower grading scenarios, low detection precision, occlusions and overlaps impact counting accuracy. This study proposed a counting method combining a deep learning algorithm with multiple viewpoints. Firstly, a flower detection model, PA-YOLO, was developed based on YOLOv5 by designing a two-scale detection branch, optimizing the number of bottlenecks and integrating a dynamic head framework. Next, PA-YOLO was used to detect grouped 360-viewpoint images of each potted plant to determine the optimal number of viewpoints for counting. The detection results indicated that PA-YOLO achieved a mean average precision (mAP) of 95.4% and an average precision (AP) of 91.9% for occluded blooms on our Phalaenopsis flower dataset. For the optimal number of viewpoints, the average counting accuracy of buds and blooms was highest at three viewpoints, with scores of 96.25% and 93.33%, respectively. The final counting accuracy reached 95.56% in flower counting tests conducted from three viewpoints. The overall results suggest that the proposed method can effectively detect and count flowers in complex occlusion and overlap environments, providing guidance for designing and implementing the vision component in an automated potted flower grading system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

Yang,

Zhang,

et al. 2024

Agronomy

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A vision transformer‐based robotic perception for early tea chrysanthemum flower counting in field environments

Qi,

Chen,

Gao

2024

Journal of Field Robotics

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The current mainstream approaches for plant organ counting are based on convolutional neural networks (CNNs), which have a solid local feature extraction capability. However, CNNs inherently have difficulties for robust global feature extraction due to limited receptive fields. Visual transformer (ViT) provides a new opportunity to complement CNNs' capability, and it can easily model global context. In this context, we propose a deep learning network based on a convolution‐free ViT backbone (tea chrysanthemum‐visual transformer [TC‐ViT]) to achieve the accurate and real‐time counting of TCs at their early flowering stage under unstructured environments. First, all cropped fixed‐size original image patches are linearly projected into a one‐dimensional vector sequence and fed into a progressive multiscale ViT backbone to capture multiple scaled feature sequences. Subsequently, the obtained feature sequences are reshaped into two‐dimensional image features and using a multiscale perceptual field module as a regression head to detect the overall scale and density variance. The resulting model was tested on 400 field images in the collected TC test data set, showing that the proposed TC‐ViT achieved the mean absolute error and mean square error of 12.32 and 15.06, with the inference speed of 27.36 FPS (512 × 512 image size) under the NVIDIA Tesla V100 GPU environment. It is also shown that light variation had the greatest effect on TC counting, whereas blurring had the least effect. This proposed method enables accurate counting for high‐density and occlusion objects in field environments and this perception system could be deployed in a robotic platform for selective harvesting and flower phenotyping.

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Sorting recognition of taproots and stem bases of Panax notoginseng using multi-objective visual tracking algorithm

Xing

Wang

San

et al. 2022

Computers and Electronics in Agriculture

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Automated flower counting from partial detections: Multiple hypothesis tracking with a connected-flower plant model

Cited by 6 publications

References 14 publications

Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

Potted Phalaenopsis Grading: Precise Bloom and Bud Counting with the PA-YOLO Algorithm and Multiviewpoint Imaging

A vision transformer‐based robotic perception for early tea chrysanthemum flower counting in field environments

Sorting recognition of taproots and stem bases of Panax notoginseng using multi-objective visual tracking algorithm

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