We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. However standard data augmentation produces only limited plausible alternative data. Data Augmentation \cite alleviates this by using existing data more effectively. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Ultimately, this paper sought to provide insight into improving imaging quality of ultra-wideband (UWB) imaging systems for plant root imaging for other works to be followed.Įffective training of neural networks requires much data. Smaller roots in difficult imaging conditions require future work into understanding and compensating for unwanted noise. Altogether, the proposed subsystems are capable of imaging and measuring concealed taproot system architectures with controlled soil conditions however, the performance of the system is highly dependent on knowledge of the soil conditions. The Image Processing and Analysis module uses a modified top-hat transformation alongside quantization methods based on energy distributions in the image to isolate the surface of the imaged root. The Image Processing and Analysis module is responsible for improving image quality and measuring root depth and average root diameter in an unsupervised manner. The Data Processing module is responsible for interpreting the measured ultra-wideband signals and producing an image using a delay-and-sum beamforming algorithm. The Data Acquisition module consists of simulated and experimental implementations of a non-contact synthetic aperture radar system to measure ultra-wideband signal reflections from concealed scattering objects in a pot containing soil. The proposed system is separated into three main subsystems: a Data Acquisition module, a Data Processing module, and an Image Processing and Analysis module. This paper presents the design, implementation, and analysis of an ultra-wideband imaging system for use in imaging potted plant root system architectures. Understanding the root system architecture of plants as they develop is critical for increasing crop yields through plant phenotyping, and ultra-wideband imaging systems have shown potential as a portable, low-cost solution to non-destructive imaging root system architectures.
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