Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning

Jin, Baichuan; Qi, Hengnian; Jia, Liangquan; Tang, Qizhe; Gao, Lu; Li, Zhenan; Zhao, Guangwu

doi:10.1016/j.infrared.2022.104097

Cited by 35 publications

(21 citation statements)

References 27 publications

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“…In order to solve the problem of seed germination rate detection, Jin et al. (2022) used the full spectrum and feature wavelengths selected by principal component analysis (PCA) to construct a convolutional neural network (CNN) and traditional machine learning methods (support vector machine (SVM) and logistic regression (LR)) for predicting the vigor of different varieties of rice seeds under natural aging conditions.…”

Section: Discussionmentioning

confidence: 99%

SGR-YOLO: a method for detecting seed germination rate in wild rice

Yao,

Zheng,

Zhou

et al. 2024

Front. Plant Sci.

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Seed germination rate is one of the important indicators in measuring seed quality and seed germination ability, and it is also an important basis for evaluating the growth potential and planting effect of seeds. In order to detect seed germination rates more efficiently and achieve automated detection, this study focuses on wild rice as the research subject. A novel method for detecting wild rice germination rates is introduced, leveraging the SGR-YOLO model through deep learning techniques. The SGR-YOLO model incorporates the convolutional block attention module (efficient channel attention (ECA)) in the Backbone, adopts the structure of bi-directional feature pyramid network (BiFPN) in the Neck part, adopts the generalized intersection over union (GIOU) function as the loss function in the Prediction part, and adopts the GIOU function as the loss function by setting the weighting coefficient to accelerate the detection of the seed germination rate. In the Prediction part, the GIOU function is used as the loss function to accelerate the learning of high-confidence targets by setting the weight coefficients to further improve the detection accuracy of seed germination rate. The results showed that the accuracy of the SGR-YOLO model for wild rice seed germination discrimination was 94% for the hydroponic box and 98.2% for the Petri dish. The errors of germination potential, germination index, and average germination days detected by SGR-YOLO using the manual statistics were 0.4%, 2.2, and 0.9 days, respectively, in the hydroponic box and 0.5%, 0.5, and 0.24 days, respectively, in the Petri dish. The above results showed that the SGR-YOLO model can realize the rapid detection of germination rate, germination potential, germination index, and average germination days of wild rice seeds, which can provide a reference for the rapid detection of crop seed germination rate.

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Section: Discussionmentioning

confidence: 99%

SGR-YOLO: a method for detecting seed germination rate in wild rice

Yao,

Zheng,

Zhou

et al. 2024

Front. Plant Sci.

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“…Seed vigor is an essential test item in the protocols for inspecting the quality of seeds due to it could accurately measure and predict the quality of seed development in the field as well as the potential germination rate, seedling emergence rate, seedling growth potential, plant resistance, and production potential. It is a key indicator for assessing the quality of seeds ( Huayta-Hinojosa et al., 2022 ; Jin et al., 2022 ; Tetreault et al., 2023 ). High-vigor seeds are a crucial assurance of successful harvests and higher agricultural product yields since they have apparent growth advantages and output potential ( Riveiro et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An RGB image dataset for seed germination prediction and vigor detection - maize

Chen,

Bai,

Wang

et al. 2024

Front. Plant Sci.

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“…Machine learning-based image processing techniques have been successfully applied to detect seed quality with the advancement of computer vision technology [6][7][8]. The researchers conduct seed quality assessment by extracting features such as texture, color and shape of the seed images.…”

Section: Introductionmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

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The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

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Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning

Cited by 35 publications

References 27 publications

SGR-YOLO: a method for detecting seed germination rate in wild rice

SGR-YOLO: a method for detecting seed germination rate in wild rice

An RGB image dataset for seed germination prediction and vigor detection - maize

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

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