Generation of 360 Degree Point Cloud for Characterization of Morphological and Chemical Properties of Maize and Sorghum

Recently, imaged-based high-throughput phenotyping methods have gained popularity in plant phenotyping. Imaging projects the 3D space into a 2D grid causing the loss of depth information and thus causes the retrieval of plant morphological traits challenging. In this study, LiDAR was used along with a turntable to generate a 360-degree point cloud of single plants. A LABVIEW program was developed to control and synchronize both the devices. A data processing pipeline was built to recover the digital surface models of the plants. The system was tested with maize and sorghum plants to derive the morphological properties including leaf area, leaf angle and leaf angular distribution. The results showed a high correlation between the manual measurement and the LiDAR measurements of the leaf area (R2\u3e0.91). Also, Structure from Motion (SFM) was used to generate 3D spectral point clouds of single plants at different narrow spectral bands using 2D images acquired by moving the camera completely around the plants. Seven narrow band (band width of 10 nm) optical filters, with center wavelengths at 530 nm, 570 nm, 660 nm, 680 nm, 720 nm, 770 nm and 970 nm were used to obtain the images for generating a spectral point cloud. The possibility of deriving the biochemical properties of the plants: nitrogen, phosphorous, potassium and moisture content using the multispectral information from the 3D point cloud was tested through statistical modeling techniques. The results were optimistic and thus indicated the possibility of generating a 3D spectral point cloud for deriving both the morphological and biochemical properties of the plants in the future. Advisor: Yufeng G

Generation of 360 Degree Point Cloud for Characterization of Morphological and Chemical Properties of Maize and Sorghum

Recently, imaged-based high-throughput phenotyping methods have gained popularity in plant phenotyping. Imaging projects the 3D space into a 2D grid causing the loss of depth information and thus causes the retrieval of plant morphological traits challenging. In this study, LiDAR was used along with a turntable to generate a 360-degree point cloud of single plants. A LABVIEW program was developed to control and synchronize both the devices. A data processing pipeline was built to recover the digital surface models of the plants. The system was tested with maize and sorghum plants to derive the morphological properties including leaf area, leaf angle and leaf angular distribution. The results showed a high correlation between the manual measurement and the LiDAR measurements of the leaf area (R2\u3e0.91). Also, Structure from Motion (SFM) was used to generate 3D spectral point clouds of single plants at different narrow spectral bands using 2D images acquired by moving the camera completely around the plants. Seven narrow band (band width of 10 nm) optical filters, with center wavelengths at 530 nm, 570 nm, 660 nm, 680 nm, 720 nm, 770 nm and 970 nm were used to obtain the images for generating a spectral point cloud. The possibility of deriving the biochemical properties of the plants: nitrogen, phosphorous, potassium and moisture content using the multispectral information from the 3D point cloud was tested through statistical modeling techniques. The results were optimistic and thus indicated the possibility of generating a 3D spectral point cloud for deriving both the morphological and biochemical properties of the plants in the future. Advisor: Yufeng G

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360◦ view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 \u3e 0.91 for individual leaf area; R2 \u3e 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D

Butyl­bis­(diphenyl­glyoximato)(pyridine-κN)­cobalt(III)1

In the title compound, [Co(C4H9)(C14H11N2O2)2(C5H5N)], the CoIII atom is coordinated by a butyl group, a nitro­gen-bonded pyridine and two N,N′-bidentate diphenyl­glyoximate ligands in a distorted octa­hedral geometry. The crystal structure features two short O—H⋯O bridges between the two chelating anions, with O⋯O distances less than 2.5 Å

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

4-[(4′-Chloro­methyl-[1,1′-biphen­yl]-4-yl)meth­yl]bis­(dimethyl­glyoximato-κ2 N,N′)(pyridine-κN)cobalt(III)1

The title compound, [Co(C14H14Cl)(C4H6N2O2)2(C5H5N)], is a model compound for the more complex cobalamines like vitamins B12. The CoIII atom is coordinated by a (4′-chloro­methyl-[1,1′-biphen­yl]-4-yl)methyl group, an N-bonded pyridine and two N,N′-bidentate dimethyl­glyoximate ligands in a distorted octa­hedral geometry. The glyoximate ligands exhibit intra­molecular O—H⋯O hydrogen bonds, which is very common in cobaloxime derivatives

Accuracy of bedside index for severity in acute pancreatitis ‘BISAP’ score in predicting outcome of acute pancreatitis

Introduction: Early identification of severe acute pancreatitis is of paramount importance in the management and for improving outcomes. Bedside index for severity in acute pancreatitis (BISAP) is a simple and accurate score for stratification in acute pancreatitis. This study was conducted to find out the accuracy of BISAP score in predicting outcomes of acute pancreatitis in local population. Method: We prospectively analyzed 96 patients with acute pancreatitis from February 2019 to December 2019. Revised Atlanta classification was used to stratify mild, moderately severe and severe pancreatitis. BISAP score was calculated within 24 hours of admission. Accuracy was measured by area under receiver operating curve (AUC). Result: Out of 96 patients, alcohol related acute pancreatitis accounted for 74.7%. There were 63.2% of mild AP, 37.3% of moderately severe AP, 9.4% of severe AP and 15.8 % of pancreatic necrosis. The AUC for moderately severe AP, severe AP and pancreatic necrosis were 0.77 (CI 0.68-0.87), 0.95 (CI 0.90-0.99) and 0.87 (CI 0.79-0.96) respectively. The statistically significant BISAP cut off for diagnosing sever AP was≥3, and ≥2 for moderately sever AP and pancreatic necrosis. There was positive correlation between revised Atlanta severity of acute pancreatitis and length of hospital stay (r=0.41). Mortality was 3.3 % which was seen in BISAP score 3 or above. Conclusion: BISAP is a simple predictive model in identifying patient at a risk of developing different severity of pancreatitis and its outcome in our population

Butyl­bis­(dimethyl­glyoximato-κ2 N,N′)(pyridine-κN)cobalt(III)1

In the title compound, [Co(C4H9)(C4H7N2O2)2(C5H5N)], which was prepared as a model complex of vitamin B12, the CoIII atom is coordinated by a butyl group, a pyridine and two N,N′-bidentate dimethyl­glyoximate ligands in a distorted octa­hedral geometry. The bis-chelating dimethyl­glyoximate ligands, which occupy equatorial sites, are linked by strong intra­molecular O—H⋯O hydrogen bonds