Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.Comment: 8 pages, 10 figures, 1 table, Accepted to IROS 202

Variability and path coefficient analysis for yield attributing traits of mungbean (Vigna radiata L.)

Seven mungbean genotypes were studied to estimate the genetic variability and path coefficient analysis for yield attributing traits at Agronomy farm of Institute of Agriculture and Animal Science (IAAS), Paklihawa Campus, Rupandehi, Nepal during summer season of 2017. The experiment was conducted with four replications in a randomized complete block design. Pant-5 and Maya were found high yielding genotypes. High genotypic coefficient of variation was exhibited by secondary branches and seed yield per plant. The low genotypic coefficient of variation was given by pod length, number of grains per pod and days to 50% flowering. High heritability was shown by test weight, secondary branches and seed yield per plant. Yield was correlated positively with days to flowering, pod length, primary branches per plant, test weight, biological, seed yield per plant and number of pods per plant. Biological yield, pod length, days to 50% flowering and no. of grains per pod contributed maximum positive and direct effect on yield indicating these three traits should be given emphasis while selecting high yielding mungbean cultivar for irrigated condition

Computational and Experimental Investigation of Runner for Gravitational Water Vortex Power Plant

The gravitational water vortex power plant (GWVPP) is a new type of low head turbine system in which a channel and basin structure is used to form a vortex, where the rotational energy from the water can be extracted through a runner. This study is focused on the optimization of the runner to improve the efficiency of the GWVPP. Computational fluid dynamics (CFD) analysis is carried out on three different runner designs with straight,twisted and curved blade profiles. ANSYS CFX was used to analyze the fluid flow through the channel, basin, turbine hub and blade, and results were used to evaluate the efficiency of each of the runner designs. The CFD analysis showed curved blade profile to be the most efficient profile, with a peak efficiency of 82%, compared to 46% for the straight blade runner and 63% for the twisted blade version. An experimental test of the turbine system was carried out to validate the runner analysis, in ascale version of the GWVPP. The testing showed that the runner behaved as predicted from the CFD analysis, and had a peak efficiency point of 71% at 0.5m head

Computational and Experimental Investigation of Runner for Gravitational Water Vortex Power Plant

The gravitational water vortex power plant (GWVPP) is a new type of low head turbine system in which a channel and basin structure is used to form a vortex, where the rotational energy from the water can be extracted through a runner. This study is focused on the optimization of the runner to improve the efficiency of the GWVPP. Computational fluid dynamics (CFD) analysis is carried out on three different runner designs with straight,twisted and curved blade profiles. ANSYS CFX was used to analyze the fluid flow through the channel, basin, turbine hub and blade, and results were used to evaluate the efficiency of each of the runner designs. The CFD analysis showed curved blade profile to be the most efficient profile, with a peak efficiency of 82%, compared to 46% for the straight blade runner and 63% for the twisted blade version. An experimental test of the turbine system was carried out to validate the runner analysis, in ascale version of the GWVPP. The testing showed that the runner behaved as predicted from the CFD analysis, and had a peak efficiency point of 71% at 0.5m head

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography–Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills