Soybean seed counting and broken seed recognition based on image sequence of falling seeds

Chen, Zeqi; Fan, Weijun; Zhang, Luo; Guo, Bin

doi:10.1016/j.compag.2022.106870

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…(2018) and Chen et al. (2022) proposed methodologies for soybean seed image segmentation. Chen et al.…”

Section: Discussionmentioning

confidence: 99%

“…counting and weighing the seeds (Chen et al, 2022), which can be time-consuming and labor-intensive. Although extensive research has been conducted to identify quantitative trait loci (QTLs) associated with this trait (Qi et al, 2020;Zhao et al, 2019), HTP studies in this area remain limited.…”

Section: Crop Sciencementioning

confidence: 99%

Section: Seed Segmentation Pipelinementioning

confidence: 99%

“…Similarly to Yang et al (2021), Wu et al (2018) and Chen et al (2022) proposed methodologies for soybean seed image segmentation. Chen et al (2022) employed a priori clustering algorithm comprising a support vector machine and k-means clustering algorithm to segment touching soybean seeds in the images; however, this study aims to investigate the recognition of broken seeds based on the sequence of images of falling seeds. Wu et al (2018) utilized a marker-controlled watershed algorithm and area threshold to count the number of seeds that are touching each other, yet neither the measurement of morphological characteristics nor the prediction of HSW by images was performed.…”

Section: Seed Segmentation Pipelinementioning

confidence: 99%

See 3 more Smart Citations

A novel image‐based approach for soybean seed phenotyping using machine learning techniques

2023

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Soybean is important for protein and oil worldwide, requiring investments in research and production technology, mainly breeding programs, to meet the increasing demand. Seed morphology, including area, perimeter, and circularity, significantly impacts soybean yield. However, the analysis of these factors has been hindered by the lack of efficient computational approaches that offer accuracy and flexibility to user preferences and needs. This study presents a soybean phenotyping system framework that includes: seed segmentation in soybean images, morphological evaluation, and image‐based prediction of hundred‐seed weight (HSW). We used genotypes from a partial diallel cross design and collected red‐green‐blue images of seeds from each plot. We developed an in‐house image processing pipeline for seed segmentation, which enabled a full morphological evaluation of the seeds. For predicting the HSW, we compared machine learning algorithms using the obtained morphological characteristics as input and features from state‐of‐the‐art convolutional neural network (CNN) architectures. Our image segmentation methodology correctly identified over 98% of the seeds in the images, even when they were in proximity. Our morphological phenotyping approach's adaptability to other plant species was verified, fully demonstrating the pipeline's generalizability. The morphological measurements were effective in predicting the HSW with an accuracy of 0.71 predictive ability and mean squared error (MSE) of 3.15 (mean HSW of 19.74). The same results were observed for CNN features, highlighting the efficiency of the morphological measurements. ResNet‐50 had the most effective feature extraction, with a mean accuracy of 0.60 (MSE of 4.15). Our study can aid automated phenotyping tool development in research and industry.

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“…(2018) and Chen et al. (2022) proposed methodologies for soybean seed image segmentation. Chen et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Crop Sciencementioning

confidence: 99%

Section: Seed Segmentation Pipelinementioning

confidence: 99%

Section: Seed Segmentation Pipelinementioning

confidence: 99%

See 2 more Smart Citations

A novel image‐based approach for soybean seed phenotyping using machine learning techniques

2023

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“…In addition, another difficulty in the separation process is concave point matching. The currently proposed matching rules for concave point pairs include the nearest-neighbor criterion [ 11 ] and the radian critical distance criterion [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An automated method for the assessment of the rice grain germination rate

et al. 2023

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The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.

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An automatic segmentation algorithm for conglutinated bone fragments in 3D CT images of lower limb fractures based on morphology

Miao,

Zheng,

Han

et al. 2024

Multimed Tools Appl

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Soybean seed counting and broken seed recognition based on image sequence of falling seeds

Cited by 14 publications

References 21 publications

A novel image‐based approach for soybean seed phenotyping using machine learning techniques

A novel image‐based approach for soybean seed phenotyping using machine learning techniques

An automated method for the assessment of the rice grain germination rate

An automatic segmentation algorithm for conglutinated bone fragments in 3D CT images of lower limb fractures based on morphology

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