Proximal Hyperspectral Image Dataset of Various Crops and Weeds for Classification via Machine Learning and Deep Learning Techniques
<h2>About the Data</h2><p dir="ltr">The data consists of proximal hyperspectral images of canola, soybean, sugarbeet, kochia, ragweed, redroot pigweed and waterhemp. The data was collected in the near infrared range of 400–1000 nm using Specim FX10 hyperspectral sensor, under controlled halogen light source. The platform and data acquisition software used for data collection was SPECIM's LabScanner system and Lumo Scanner respectively. The raw hyperspectral images were reference calibrated using the white and dark reference image. The hyperspectral images are saved as Numpy Array (.npy) files in their respective directories. Support Jupyter Notebooks provide additional tools for augmentation, region of interest selection, and spectral preprocessing.</p><h2>Benefit of Data</h2><ol><li>Data can enhance the number of data points for machine learning and deep learning models, aiding in classification or identification tasks.</li><li>It can serve as a valuable instrument for studies in spectroscopy.</li><li>It can assist in the development and testing of three-dimensional data models.</li></ol><h2>Dataset Information</h2><p dir="ltr">Each plant consists of 20 images, each image having four plants. Except in the case of redroot pigweed which has one plant/image and consists of 40 images.</p><p dir="ltr"><b>Number of images:</b></p><ol><li>canola = 20</li><li>soybean = 20</li><li>sugarbeet = 20</li><li>kochia = 20</li><li>ragweed = 20</li><li>redroot_pigweed = 40</li><li>water hemp = 20</li></ol><p><br></p>
| Main Authors: | , , , , |
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| Format: | Dataset biblioteca |
| Published: |
2024
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| Subjects: | Crop and pasture protection (incl. pests, diseases and weeds), Agricultural engineering, Machine learning, Deep learning, hyperspectral, machine learning, deep learning, weed classification, |
| Online Access: | https://figshare.com/articles/media/Proximal_Hyperspectral_Image_Dataset_of_Various_Crops_and_Weeds_for_Classification_via_Machine_Learning_and_Deep_Learning_Techniques/25306255 |
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| Summary: | <h2>About the Data</h2><p dir="ltr">The data consists of proximal hyperspectral images of canola, soybean, sugarbeet, kochia, ragweed, redroot pigweed and waterhemp. The data was collected in the near infrared range of 400–1000 nm using Specim FX10 hyperspectral sensor, under controlled halogen light source. The platform and data acquisition software used for data collection was SPECIM's LabScanner system and Lumo Scanner respectively. The raw hyperspectral images were reference calibrated using the white and dark reference image. The hyperspectral images are saved as Numpy Array (.npy) files in their respective directories. Support Jupyter Notebooks provide additional tools for augmentation, region of interest selection, and spectral preprocessing.</p><h2>Benefit of Data</h2><ol><li>Data can enhance the number of data points for machine learning and deep learning models, aiding in classification or identification tasks.</li><li>It can serve as a valuable instrument for studies in spectroscopy.</li><li>It can assist in the development and testing of three-dimensional data models.</li></ol><h2>Dataset Information</h2><p dir="ltr">Each plant consists of 20 images, each image having four plants. Except in the case of redroot pigweed which has one plant/image and consists of 40 images.</p><p dir="ltr"><b>Number of images:</b></p><ol><li>canola = 20</li><li>soybean = 20</li><li>sugarbeet = 20</li><li>kochia = 20</li><li>ragweed = 20</li><li>redroot_pigweed = 40</li><li>water hemp = 20</li></ol><p><br></p> |
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