Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments
Early detection of nitrogen deficiency in cotton requires timely identification of stress symptoms like leaf chlorosis (yellowing) and canopy stunting. Chlorosis initially appears in older, lower-canopy leaves, which are often not visible in conventional nadir-looking imaging. This study investigate...
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MDPI AG
2025-02-01
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| author | Vaishali Swaminathan J. Alex Thomasson Nithya Rajan Robert G. Hardin |
| author_facet | Vaishali Swaminathan J. Alex Thomasson Nithya Rajan Robert G. Hardin |
| author_sort | Vaishali Swaminathan |
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| description | Early detection of nitrogen deficiency in cotton requires timely identification of stress symptoms like leaf chlorosis (yellowing) and canopy stunting. Chlorosis initially appears in older, lower-canopy leaves, which are often not visible in conventional nadir-looking imaging. This study investigates oblique ground-based multispectral imaging to estimate plant height and capture spectral details from the upper (UC) and lower (LC) cotton canopy layers. Images were collected from four camera pitch and height configurations: set 1 (30°, 2 m), set 2 (55°, 2 m), set 3 (68°, 3 m), and set 4 (70°, 1.5 m). A pre-trained monocular depth estimation model (MiDaS) was used to estimate plant height from aligned RGB images and an empirically derived tangential model corrected for perspective distortion. Further, the lower and upper vertical halves of the plants were categorized as LC and UC, with vegetation indices (<i>CI<sub>green</sub>, CI<sub>rededge</sub></i>) calculated for each. The aligned images in set 1 had the best sharpness and quality. The plant height estimates from set 1 had the highest correlation (r = 0.64) and lowest root mean squared error (RMSE = 0.13 m). As the images became more oblique, alignment and monocular depth/height accuracy decreased. Also, the effects of perspective and object-scale ambiguity in monocular depth estimation were prominent in the high oblique and relatively low altitude images. The spectral vegetation indices (VIs) were affected by band misalignment and shadows. VIs from the different canopy layers demonstrated moderate correlation with leaf nitrogen concentration, and sets 2 and 3 specifically showed high and low differences in VIs from the UC and LC layers for the no and high-nitrogen treatments, respectively. However, improvements in the multispectral alignment process, extensive data collection, and ground-truthing are needed to conclude whether the LC spectra are useful for early nitrogen stress detection in field cotton. |
| format | Article |
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| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
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| series | Remote Sensing |
| spelling | doaj-art-6aa656d144364c038134ba5bc7b6523b2025-08-20T02:03:30ZengMDPI AGRemote Sensing2072-42922025-02-0117457910.3390/rs17040579Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen TreatmentsVaishali Swaminathan0J. Alex Thomasson1Nithya Rajan2Robert G. Hardin3Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USADepartment of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USAEarly detection of nitrogen deficiency in cotton requires timely identification of stress symptoms like leaf chlorosis (yellowing) and canopy stunting. Chlorosis initially appears in older, lower-canopy leaves, which are often not visible in conventional nadir-looking imaging. This study investigates oblique ground-based multispectral imaging to estimate plant height and capture spectral details from the upper (UC) and lower (LC) cotton canopy layers. Images were collected from four camera pitch and height configurations: set 1 (30°, 2 m), set 2 (55°, 2 m), set 3 (68°, 3 m), and set 4 (70°, 1.5 m). A pre-trained monocular depth estimation model (MiDaS) was used to estimate plant height from aligned RGB images and an empirically derived tangential model corrected for perspective distortion. Further, the lower and upper vertical halves of the plants were categorized as LC and UC, with vegetation indices (<i>CI<sub>green</sub>, CI<sub>rededge</sub></i>) calculated for each. The aligned images in set 1 had the best sharpness and quality. The plant height estimates from set 1 had the highest correlation (r = 0.64) and lowest root mean squared error (RMSE = 0.13 m). As the images became more oblique, alignment and monocular depth/height accuracy decreased. Also, the effects of perspective and object-scale ambiguity in monocular depth estimation were prominent in the high oblique and relatively low altitude images. The spectral vegetation indices (VIs) were affected by band misalignment and shadows. VIs from the different canopy layers demonstrated moderate correlation with leaf nitrogen concentration, and sets 2 and 3 specifically showed high and low differences in VIs from the UC and LC layers for the no and high-nitrogen treatments, respectively. However, improvements in the multispectral alignment process, extensive data collection, and ground-truthing are needed to conclude whether the LC spectra are useful for early nitrogen stress detection in field cotton.https://www.mdpi.com/2072-4292/17/4/579terrestrial platformsremote sensingcomputer visionheight from depth |
| spellingShingle | Vaishali Swaminathan J. Alex Thomasson Nithya Rajan Robert G. Hardin Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments Remote Sensing terrestrial platforms remote sensing computer vision height from depth |
| title | Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments |
| title_full | Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments |
| title_fullStr | Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments |
| title_full_unstemmed | Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments |
| title_short | Extracting Features from Oblique Ground-Based Multispectral Images for Monitoring Cotton Physiological Response to Nitrogen Treatments |
| title_sort | extracting features from oblique ground based multispectral images for monitoring cotton physiological response to nitrogen treatments |
| topic | terrestrial platforms remote sensing computer vision height from depth |
| url | https://www.mdpi.com/2072-4292/17/4/579 |
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