Maize seed appearance quality assessment based on improved Inception-ResNet

Song, Chang; Peng, Bo; Wang, Huanyue; Zhou, Yuhong; Sun, Lei; Suo, Xuesong; Fan, Xiaofei

doi:10.3389/fpls.2023.1249989

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Traditional sensory analysis methods primarily focus on grain weight, grain thickness, volume, density, color, gloss, and other phenotypic traits [4]. However, these parameters heavily rely on subjective human experience and have relatively high technical requirements, leading to low detection efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

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The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

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Section: Introductionmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

View full text Add to dashboard Cite

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Research Progress on Seed Appearance Recognition for Major Crops

Yun,

Li,

et al. 2024

Smart Agriculture

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Research on lightweight detection algorithm for millet quality grading

Wang,

Shao,

Tian

et al. 2024

J Food Process Engineering

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Accurately assessing the quality of millet is of paramount importance for its storage, processing, and consumption. In complex environments, rapidly and precisely identifying densely packed millet poses a significant challenge in the quality grading process. This paper introduces an improved version of the YOLOv7 lightweight detection algorithm. The key enhancement involves integrating the super lightweight target detection algorithm ESNet. ESNet optimizes the lightweight design of the neck by enhancing the backbone network, thereby strengthening the network's feature extraction capabilities. Furthermore, by combining Soft non‐maximum suppression (NMS) with the EIOU Loss function, the accuracy of detecting dense millet in occluded scenarios is further optimized. Various models were evaluated using the millet dataset in this study. During the testing phase, images containing densely packed millet were randomly selected to assess the effectiveness of the models. The test results demonstrate that, compared to lightweight network architectures such as LCNet, ShuffleNetV2, and MobileNetV3, the improved YOLOv7‐ESNet model achieves higher average accuracy, precision, recall, and comprehensive evaluation metrics in detection. Additionally, by combining the YOLOv7‐ESNet model with Soft‐EIoU NMS, an average accuracy of 93.19% and an average prediction time of 52.5 ms were achieved. These results meet the real‐time grading requirements for millet quality, establishing a robust technical foundation for millet quality grading. This paper overcomes the challenge of precise millet detection in complex scenarios, providing a valuable contribution to the field.

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Maize seed appearance quality assessment based on improved Inception-ResNet

Cited by 4 publications

References 44 publications

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Research Progress on Seed Appearance Recognition for Major Crops

Research on lightweight detection algorithm for millet quality grading

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