A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables.

Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.Published onlin

Local-global splitting for spatiotemporal-adaptive multiscale methods

We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations

Accuracy assessment in glacier change analysis

This thesis assesses the accuracy of digital elevation models (DEM) generated from contour lines and LiDAR points (Light Detection and Ranging) employing several interpolation methods at different resolutions. The study area is Jostefonn glacier that is situated in Sogn og Fjordane county, Norway. There are several ways to assess accuracy of DEMs including simple ways such as visual comparison and more sophisticated methods like relative and absolute comparison. Digital elevation models of the Jostefonn glacier were created from contour lines for years 1966 and 1993. LiDAR data from year 2011 was used as a reference data set. Of all the interpolation methods tested Natural Neighbours (NN) and Triangular Irregular Network (TIN) algorithms rendered the best results and proved to be superior to other interpolation methods. Several resolutions were tested (the cell size of 5 m, 10 m, 20 m and 50 m) and the best outcome was achieved by as small cell size as possible. The digital elevation models were compared to a reference data set outside the glacier area both on a cell-by-cell basis and extracting information at test points. Both methods rendered the same results that are presented in this thesis. Several techniques were employed to assess the accuracy of digital elevation models including visualization and statistical analysis. Visualization techniques included comparison of the original contour lines with those generated from DEMs. Root mean square error, mean absolute error and other accuracy measures were statistically analysed. The greatest elevation difference between the digital elevation model of interest and the reference data set was observed in the areas of a steep terrain. The steeper the terrain, the greater the observed error. The magnitude of the errors can be reduced by using a smaller cell size but that this is offset by a larger amount of data and increased data processing time.Popular science Glaciers are very sensitive indicators of climate change. The major cause of melting glaciers is global warming. This rapid rate of melting has serious negative impact on the earth causing flooding, leaving impact on flora and fauna, resulting in shortage of freshwater and hydroelectricity. The long-term monitoring of glaciers and the knowledge gained from it can help governments, environmental and water resource managers to make plans to cope with impacts of climate change. Results from glacier monitoring ought to be precise, showing the actual situation compared to the situation in the past as well as predicting possible glacier changes in the future. The aim of this thesis was to investigate how sensitive the results were to different methods used in glacier change detection focusing on the quality of Digital Elevation Models (DEMs). The study area of this thesis was the Jostefonn glacier situated in Sogn and Fjordane county, Norway. Digital elevation models were created from contour lines for years 1966 and 1993. LiDAR data from year 2011 was used as a reference data set. Several techniques were employed to estimate the accuracy of digital elevation models including visualization, statistical analysis, analysing the accuracy of digital elevation models for terrain on different slopes, comparison to a reference data set outside the glacier area that was considered to be stable and where no elevation change was expected. The original contour lines (1966 and 1993) were compared with the ones generated from the created terrain models (glacier area) as well as with the contour lines from the reference data set (outside the glacier area) by visualization techniques. Accuracy measures (Root Mean Square Error, Mean Absolute Error and others) were statistically analysed. Natural Neighbours and Triangular Irregular Network interpolators proved to be superior to other algorithms used to create the terrain models. The best outcome was achieved by using as small cell size as possible. 5 m resolution rendered the best results from the resolutions tested (5 m, 10 m, 20 m and 50 m). The greatest elevation differences were observed in the areas of a steep terrain. The steeper the terrain, the greater the elevation difference. The terracing effect was noticed in the digital elevation models due to the high density of elevation points on the contour lines and hardly any points between them. Useful information can be obtained by estimating accuracy of digital elevation models. The accuracy of terrain models determines the reliability of glacier change analysis and that is why the digital elevation model must represent the terrain as accurately as possible. The different methods used in this thesis rendered very similar results and that indicated that the results were reliable and the terrain models created with Natural Neighbours and Triangular Irregular Network interpolators (resolution of 5 m) can be employed in further glacier change analysis

Workshop on Advanced Technologies for Planetary Instruments, part 1

This meeting was conceived in response to new challenges facing NASA's robotic solar system exploration program. This volume contains papers presented at the Workshop on Advanced Technologies for Planetary Instruments on 28-30 Apr. 1993. This meeting was conceived in response to new challenges facing NASA's robotic solar system exploration program. Over the past several years, SDIO has sponsored a significant technology development program aimed, in part, at the production of instruments with these characteristics. This workshop provided an opportunity for specialists from the planetary science and DoD communities to establish contacts, to explore common technical ground in an open forum, and more specifically, to discuss the applicability of SDIO's technology base to planetary science instruments

On the use of artificial neural networks in remotely piloted aircraft acquired images for estimating reservoir’s bathymetry

The use of acoustic systems for mapping submerged areas is the most accurate way. However, echosounders are expensive and, in addition, the equipment requires a great deal of experience on the part of the specialist. From another perspective, orbital and aerial images (acquired by RPA’s- Remotely Piloted Aircraft) can offer bathymetric maps of larger locations that are difficult to access at a low operating cost. Therefore, the present study’s main objective was to evaluate the utility of RGB images obtained with RPA’s in water reservoirs. Thus, Artificial Neural Networks were used for depth training and prediction. Subsequently, it compared to the bathymetric data from the same pond in question, raised from acoustic sensors, quantifying the vertical uncertainty through three estimators. Regarding the statistical analysis, the RMSE and Ф estimators showed better reliability. The 300-point sample showed the best quality in processing. The results showed that the methodology could improve the management of water resources. The method allows reduced execution time and lowers cost, especially for using only the green, red and blue channels, easily found in most cameras coupled to RPA’s

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer