As datasets grow, the need for automated methods to ensure dataset quality arises. This report presents an experiment conducted on the MSCOCO train2017 dataset to identify image outliers using a force-directed graph built from a co-occurrence context, focusing on the mean average precision and average precision. The experiment involved placing anomaly scores on images using Euclidean distance and k-means clustering, creating subsets where a percentage of images withthe highest anomaly scores were removed. You Only Look Once version 8 models were trained on each subset, and the results showed a promising increase in performance compared to randomlyr emoving images. However, the increase was relatively small, and further research is needed. Interms of future work, other methods of identifying outliers, other datasets, and investigating the uses of contextual information in other areas are discussed.

The aim of this project is to design an algorithm that identifies the Area of Interest (AOI) in ELISpot and FluoroSpot images. ELISpot and FluoroSpot are two varieties of a biochemical test used to analyze immune responses by quantifying the amount of cytokine secreted by cells. ELISpot and FluoroSpot images show a well that contains the cytokinesecreting cells which appear as scattered spots. Prior to counting the number of spots, it is required to detect the area in which to count the spots, i.e. the area delimited by the contour of the well. We propose to use the Circle Hough Transform together with filtering and the Laplacian of Gaussian edge detector in order to accurately detect such area. Furthermore we develop an outlier removal method that contributes to increase the robustness of the proposed detection method. Finally we compare our algorithm with another algorithm already in use. A Swedish biotech company called Mabtech has implemented an AOI identifier in the same field. Our proposed algorithm proves to be more robust and provides consistent results for all the images in the dataset.

<div>Leaf Area Index (LAI) is an important variable for both for characterizing plant canopy and as an input to many crop models. It is a dimensionless quantity broadly defined as the total one-sided leaf area per unit ground area, and is estimated over agriculture row crops by both direct and indirect methods. Direct methods, which involve destructive sampling, are laborious and time-consuming, while indirect methods such as remote sensing-based approaches have multiple sources of uncertainty. LiDAR (Light Detection and Ranging) remotely sensed data acquired from manned aircraft and UAVs’ have been investigated to estimate LAI based on physical/geometric features such as canopy gap fraction. High-resolution point cloud data acquired with a laser scanner from any platform, including terrestrial laser scanning and mobile mapping systems, contain random noise and outliers. Therefore, outlier detection in LiDAR data is often useful prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, description of plant complexity, and local point densities, which are crop dependent. This dissertation first explores the effectiveness of using LiDAR data to estimate LAI for row crop plants at multiple times during the growing season from both a wheeled vehicle and an Unmanned Aerial Vehicle (UAV). Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data and ground reference obtained from an in-field plant canopy analyzer and leaf area derived from destructive sampling. LAI estimates obtained from support vector regression (SVR) models with a radial basis function (RBF) kernel developed using the wheel-based LiDAR system and UAVs are promising, based on the value of the coefficient of determination (R2) and root mean squared error (RMSE) of the residuals. </div><div>This dissertation also investigates approaches to minimize the impact of outliers on discrete return LiDAR acquired over crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform. Two methods are explored to detect and remove the outliers from the plant datasets. The first is based on surface fitting to noisy point cloud data based on normal and curvature estimation in a local neighborhood. The second utilizes the deep learning framework PointCleanNet. Both methods are applied to individual plants and field-based datasets. To evaluate the method, an F-score and LAI are calculated both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust to changes in point densities, level of noise, and shapes. Also, the predicted LAI was improved for the wheel-based vehicle data based on the R2 value and RMSE of residuals. </div><div>The quality of the extracted features depends on the point density and laser penetration of the canopy. Extracting appropriate features is a critical step to have accurate prediction models. Deep learning frameworks are increasingly being used in remote sensing applications. In the last objective of this study, a feature extraction approach is investigated for encoding LiDAR data acquired by UAV platforms multiple times during the growing season over sorghum and maize plant breeding experiments. LAI estimates obtained with these inputs are used to develop support vector regression (SVR) models using plant canopy analyzer data as the ground reference. Results are compared to models based on estimates from physically-based features and evaluated in terms of the coefficient determination (R2). The effects of experimental conditions, including flying height, sensor characteristics, and crop type, are also investigated relative to the estimates of LAI.</div><div><br></div>