High-throughput corn ear screening method based on two-pathway convolutional neural network

Zhang, JiaJing; Ma, Qin; Cui, Xuelian; Guo, Hao; Wang, Ke; Zhu, Ding

doi:10.1016/j.compag.2020.105525

Cited by 19 publications

(13 citation statements)

References 39 publications

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“…In this article, RGB and NIR images ( Kusumaningrum et al, 2018 ) collected by a multispectral camera were used to train a CNN model. To solve the problem of corn seed adhesion and seed location during the recognition process, a watershed algorithm ( Lei et al, 2019 ; Sta et al, 2019 ; Zhang et al, 2021 ) combined with a two-way CNN ( Zhang J. J. et al, 2020 ) was proposed to detect corn seed defects. The results revealed that this method is with high accuracy, and the targets can be accurately located and classified.…”

Section: Introductionmentioning

confidence: 99%

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

et al. 2022

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Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.

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Section: Introductionmentioning

confidence: 99%

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

et al. 2022

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“…To detect wheat spikes, the remote sensing imaging devices are useful tools to replace traditional artificial detection ( Aparicio et al, 2000 ). Hyperspectral imaging cameras can provide rich spectral information for wheat detection ( Shen et al, 2021 ), but the cost of hyperspectral imaging is expensive, which restricts the application in various fields ( Zhang et al, 2020 ). Thus, the cheaper RGB imaging camera is a realistic alternative to achieve effective wheat detection.…”

Section: Introductionmentioning

confidence: 99%

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

Zhang

Steffenson

et al. 2022

Front. Plant Sci.

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Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

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“…For example, a two‐pathway CNN model combining the advantages of VGG‐16 and Resnet‐50 can greatly reduce the number of input parameters. The two‐pathway CNN got a 97.23% accuracy in corn ear Screening (Zhang et al., 2020). An online sorting system of deep CNN was used to grade bell peppers into five classes based on appearance inspection with accuracy of 96.9% (Mohi‐Alden et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

X‐ray‐based machine vision technique for detection of internal defects of sterculia seeds

Xue

Miao²,

Miao

et al. 2022

Journal of Food Science

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An online machine learning system based on X‐ray nondestructive quality evaluation technique was developed to detect internal defects of boat‐fruited sterculia seed. The X‐ray images of boat‐fruited sterculia seed were first acquired by the detection system. Then, a boat‐fruited sterculia seed net (BSSNet) was trained to identify the defective boat‐fruited sterculia seeds based on the X‐ray images. The BSSNet was evaluated with the accuracy, precision, specificity, and sensitivity as 94.64%, 93.51%, 92.37%, and 96.64%, respectively. Further, three classical CNN models including VGG16, Resnet, and Inception were trained on the same dataset with accuracy of 95.71%, 94.29%, and 94.64%, respectively. Compared with the classical CNN models, the BSSNet achieved similar or higher accuracy in X‐ray images classification. Finally, an independent dataset containing 200 X‐ray images was used to validate the performance of the BSSNet and obtained an accuracy of 96.5%. The results presented above demonstrated that this classification method has a great potential for industrial applications. Practical Application An X‐ray online detection system integrated with a machine vision model was used to evaluate the quality of boat‐fruited sterculia seed. A low‐power x‐ray detection system can detect internal defects of the object and ensure safety in the production process. The developed machine vision can sort the boat‐fruited sterculia seed with an accuracy of 96.5%. The proposed nondestructive detection system showed a good potential to be used in industrial applications.

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High-throughput corn ear screening method based on two-pathway convolutional neural network

Cited by 19 publications

References 39 publications

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

X‐ray‐based machine vision technique for detection of internal defects of sterculia seeds

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