A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures

Díaz-Martínez, Víctor; Orozco-Sandoval, Jairo; Manian, Vidya; Dhatt, Balpreet K.; Walia, Harkamal

doi:10.3390/s23094370

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…This makes it challenging to obtain accurate recognition in complex situations. In recent years, deep learning methods have gradually been applied to the field of agricultural production ( Díaz-Martínez et al, 2023 ). Mi et al (2020) constructed a deep learning network, C-DenseNet, incorporating the convolutional block attention module (CBAM) attention mechanism to classify wheat stripe rust.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Yang,

Sun,

Wang

et al. 2024

PeerJ Computer Science

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Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

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Section: Introductionmentioning

confidence: 99%

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Yang,

Sun,

Wang

et al. 2024

PeerJ Computer Science

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“…Especially, this technology can rapidly and non-destructively analyze the internal structure and chemical composition of samples. Thus, it has gained popularity in seed inspection [15][16][17]. Many researchers have analyzed the relationship between spectral and image information and predicted attributes to establish models for seed cultivar identification [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Cotton seed cultivar identification based on the fusion of spectral and textural features

Liu,

Guo,

et al. 2024

PLoS ONE

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The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm—1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800–1000 nm), red region (620–700 nm), and blue-green region (420–570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

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“…They also used IR-HSI, LeNet, GoogLeNet, and ResNet to identify rice seed varieties, among which the classification effect of the ResNet model is the best, and the classification accuracy of the test set is 86.08%. Diaz-Martinez et al [19] proposed a framework for rice seed hyperspectral image processing and classification that combined two powerful tools of hyperspectral imaging and deep learning. 3D-CNN was used to classify five seeds with different processing and six seeds with different high temperature treatments.…”

Section: Introductionmentioning

confidence: 99%

Classification of Rice Seeds Grown in Different Geographical Environments: An Approach Based on Improved Residual Networks

Yu,

Chen,

Song

et al. 2024

Agronomy

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Rice is one of the most important crops for food supply, and there are multiple differences in the quality of rice in different geographic regions, which have a significant impact on subsequent yields and economic benefits. The traditional rice identification methods are time-consuming, inefficient, and delicate. This study proposes a deep learning-based method for fast and non-destructive classification of rice grown in different geographic environments. The experiment collected rice with the name of Ji-Japonica 830 from 10 different regions, and a total of 10,600 rice grains were obtained, and the fronts and backsides of the seeds were photographed with a camera in batches, and a total of 30,000 images were obtained by preprocessing the data. The proposed improved residual network architecture, High-precision Residual Network (HResNet), was used to compare the performance of the models. The results showed that HResNet obtained the highest classification accuracy result of 95.13%, which is an improvement of 7.56% accuracy with respect to the original model, and validation showed that HResNet achieves a 98.7% accuracy in the identification of rice grown in different soil classes. The experimental results show that the proposed network model can effectively recognize and classify rice grown in different soil categories. It can provide a reference for the identification of other crops and can be applied for consumer and food industry use.

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A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures

Cited by 9 publications

References 20 publications

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Cotton seed cultivar identification based on the fusion of spectral and textural features

Classification of Rice Seeds Grown in Different Geographical Environments: An Approach Based on Improved Residual Networks

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