Plant phenotyping is the complete evaluation of complex plant traits such as its growth, development, tolerance or resistance, measured on the basis of quantitative and individual parameters of the plant itself. The evaluation process should be automated and non-destructive, suggesting computer vision as a key enabling technology to perform this task. In this paper, we propose a computer vision software pipeline for the analysis of the roots system of a plant. Two main contributions are provided: first, a deterministic procedure to assemble a roots panorama image starting from multiple shots of a rotating rhizotron; second, the automatic extraction of a binary mask representing the observed roots in the image. Results on more than 20.000 RGB images demonstrate the robustness and feasibility of our approach, reporting 77% median sensitivity and 99% median specificity in the roots segmentation task. This study can be seen as the first step towards the automation of labelled data to be used in complex deep learning architectures devoted to higher level applications, such as the automatic data-driven feature extraction as well as high-throughput applications.
In this study we analyzed deep learning methods for point clouds semantic segmentation. We compared PointNet and PointNet++ on data with different characteristics, coming from distinct domains, in order to understand their behavior. Finally, we exploited the so gained knowledge to improve the performance of the models on railway data. In particular, we properly updated the training protocol and altered the PointNet++ architecture, in order to perform transfer learning by leveraging the models previously trained in the first experiments. Results on both state-of-the-art datasets and on a custom dataset specifically acquired for this scope demonstrate that transfer learning can effectively boost the performance of the models in terms of prediction accuracy and convergence rate in the railway context.
Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinary applications that are being developed in the last decades. AI approaches’ ability of extracting relevant information from data is essential to perform comprehensive studies in new multidisciplinary topics such as ecological informatics. For example, improving knowledge on cetaceans’ distribution patterns enables the acquisition of a strategic expertise for developing tools aimed to the preservation of the marine environment. In this paper we present an innovative approach, based on Random Forest and RUSBoost, aimed to define predictive models for presence/absence and abundance estimation of two classes of cetaceans: the striped dolphin Stenella coeruleoalba and the common bottlenose dolphin Tursiops truncatus. Sightings data from 2009 to 2017 have been collected and enriched by geo-morphological and meteorological data in order to build a comprehensive dataset of real observations used to train and validate the proposed algorithms. Results in terms of classification and regression accuracy demonstrate the feasibility of the proposed approach and suggest the application of such artificial intelligence based techniques to larger datasets, with the aim of enabling large scale studies as well as improving knowledge on data deficient species.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.