Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology

Zhang, Shuheng; Zeng, Hanguo; Jiang, Wei; Yi, Kun; Yang, Shangqi; Mao, Peisheng; Wang, Zhanjun; Yu, Hao; Li, Manli

doi:10.3390/s22072760

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Multispectral imaging has been demonstrated to be effective in differentiating seeds based on their morphological and spectral features ( Hu et al., 2020 ). This study confirmed the morphological differences observed in previous reports during the maturation process of seeds ( Zhang et al., 2022 ), where the projected area, length, and width of seeds increased with increasing maturity ( Harada, 1997 ). However, these morphological traits were not sufficient to accurately determine seed maturity due to the differences between seeds with similar morphological features.…”

Section: Discussionsupporting

confidence: 92%

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Jia

Sun

et al. 2023

Front. Plant Sci.

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Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.

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Section: Discussionsupporting

confidence: 92%

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Jia

Sun

et al. 2023

Front. Plant Sci.

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“…Despite the reliability of traditional germination and vigor tests in meeting seed production standards, they often fall short in predicting seedling emergence under less favorable environmental conditions [7]. Moreover, these tests can be time-consuming, costly, and offer limited insights into the internal state of the seed [8]. Addressing this challenge, the assessment of internal seed morphology has emerged as a crucial approach for identifying issues related to seed physiological potential [6,9].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research in the literature has explored various computational methods that correlate image-derived parameters with seed quality standards [5,10,11]. Image analysis techniques provide non-destructive means to comprehend various aspects of seed development, establishing a connection between internal morphology and structural integrity [8,[12][13][14]. This approach enables the determination of the physiological potential of seed lots.…”

Section: Introductionmentioning

confidence: 99%

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

da Silva,

de Freitas,

Abud

et al. 2024

AgriEngineering

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Seed quality significantly affects how well crops grow. Traditional methods for checking seed quality, like seeing how many seeds sprout or using a chemical test called tetrazolium testing, require people to look at the seeds closely, which takes a lot of time and effort. Nowadays, computer vision, a technology that helps computers see and understand images, is being used more in farming. Here, we use computer vision with X-ray imaging to assist experts in rapidly and accurately assessing seed quality. We looked at three different sets of seeds using X-ray images and used YOLOv8 to analyze them. YOLOv8 software measures different aspects about seeds, like their size and the area taken up by the part inside, called the endosperm. Based on this information, we put the seeds into four groups depending on how much endosperm they have. Our results show that the YOLOv8 program works well in identifying and separating the endosperm, even with a small amount of data. Our method was able to accurately identify the endosperm about 95.6% of the time. This means that our approach can help determine how effective the seeds are to plant crops.

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Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

Lv,

Liang,

Tian

et al. 2024

Journal of Biophotonics

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Thymoma, a rare tumor from thymic epithelial cells, presents diagnostic challenges because of the subjective nature of traditional methods, leading to high false‐negative rates and long diagnosis times. This study introduces a thymoma classification technique that integrates hyperspectral imaging with deep learning. We initially capture pathological slice images of thymoma using a hyperspectral camera and delineate regions of interest to extract spectral data. This data undergoes reflectance calibration and noise reduction. Subsequently, we transform the spectral data into two‐dimensional images via the Gramian Angular Field (GAF) method. A variant residual network is then utilized to extract features and classify these images. Our results demonstrate that this model significantly enhances classification accuracy and efficiency, achieving an average accuracy of 95%. The method proves highly effective in automated thymoma diagnosis, optimizing data utilization, and feature representation learning.

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Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology

Cited by 11 publications

References 15 publications

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

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