248 research outputs found
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
No full text<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p>
<p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p>
<p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p>
<p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p>
<p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p>
<p>To load these files into your Python environment, you can use the following code snippet:</p>
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<div>th4figs = '/content/drive/MyDrive/compag_2023/'</div>
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<div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div>
<div>Xtrain = np.load(path4images)</div>
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<div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div>
<div>Ytrain = np.load(path4labels).astype(float)</div>
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<div>print("X train : ", Xtrain.shape)</div>
<div>print("Y train : ", Ytrain.shape)</div>
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<pre>X train : (5332, 300, 300, 3)
Y train : (5332,)<br>
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</div><p>If you want to cite this work you can use the following text:</p>
<p>@article{TEMENOS2024101174,<br>title = {Goat-CNN: A lightweight convolutional neural network for pose-independent body condition score estimation in goats},<br>journal = {Journal of Agriculture and Food Research},<br>volume = {16},<br>pages = {101174},<br>year = {2024},<br>issn = {2666-1543},<br>doi = {https://doi.org/10.1016/j.jafr.2024.101174},<br>url = {https://www.sciencedirect.com/science/article/pii/S2666154324002114},<br>author = {Anastasios Temenos and Athanasios Voulodimos and Vera Korelidou and Athanasios Gelasakis and Dimitrios Kalogeras and Anastasios Doulamis and Nikolaos Doulamis},<br>keywords = {Body condition score, Artificial intelligence, Convolutional neural network, Precision livestock farming, Goat, Animal, Signal processing, Computer vision},<br>abstract = {Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.}<br>}</p>
<p>Anastasios Temenos, Athanasios Voulodimos, Vera Korelidou, Athanasios Gelasakis, Dimitrios Kalogeras, Anastasios Doulamis, Nikolaos Doulamis,<br>Goat-CNN: A lightweight convolutional neural network for pose-independent body condition score estimation in goats,<br>Journal of Agriculture and Food Research,<br>Volume 16,<br>2024,<br>101174,<br>ISSN 2666-1543,<br>https://doi.org/10.1016/j.jafr.2024.101174.<br>(https://www.sciencedirect.com/science/article/pii/S2666154324002114)<br>Abstract: Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.<br>Keywords: Body condition score; Artificial intelligence; Convolutional neural network; Precision livestock farming; Goat; Animal; Signal processing; Computer vision</p>
Data Set of PLOS Computational Paper PCOMPBIOL-D-18-02181R1
No full textFigures Data of PLOS Computational paper:Modeling of the axon plasma membrane structure and its effects on protein diffusionAuthors: Yihao Zhang, Anastasios V. Tzingounis, and George LykotrafitisCorresponding Author: George Lykotrafitis, Ph.D.University of ConnecticutStorss, CT UNITED STATES</div
The state of modern Greek language as spoken in Victoria
No full textDeposited with permission of the author. © 1986 Dr. Anastasios TamisThis thesis reports a sociolinguistic study, carried out between 1981 and 1984, of the state of the Modern Greek (MG) language in Australia, as spoken by native-speaking first-generation Greek immigrants in Victoria. Particular emphasis is given to the analysis of those characteristics of the linguistic behaviour of these Greek Australians which can be attributed to the contact with English and to other environmental, social and linguistic influence. (For complete abstract open document
On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net
No full textDetecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks. In this work, we explore the effectiveness of the CNN-based architecture U-Net and its variations, namely, the Residual U-Net, the Attention U-Net, and the Attention Residual U-Net, in automatic building extraction. We showcase their robustness in feature extraction and information processing using exclusively RGB images, as they are a low-cost alternative to multi-spectral and LiDAR ones, selected from the SpaceNet 1 dataset. The experimental results show that U-Net achieves a 91.9% accuracy, whereas introducing residual blocks, attention gates, or a combination of both improves the accuracy of the vanilla U-Net to 93.6%, 94.0%, and 93.7%, respectively. Finally, the comparison between U-Net architectures and typical deep learning approaches from the literature highlights their increased performance in accurate building localization around corners and edges
New historical evidence for Anastasios Emm. Papas
No full textNo AbstractThe author’s attention has been drawn to the existence of this historicalevidence in the National Archives of Vienna, by his friend the writer EteoclesGregoriadis together with the numbers of the relevant files. Most of the documents were written in the old German script. Thus the author asked for the help of his friend and former colleague at the University of Thessaloniki and director of the Goethe Institute, Graf Kurt v. Posadowsky, for reading andstudying those documents. Without his help this study would have been impossible. This new evidence concerns the sojourn of Anastasios Papas·—son of Emmanuel Papas, leading figure of the Greek Revolution—in Austria andGermany between the 3rd January and 11th March 1822. There is informationabout his short imprisonment in Trieste, after his arival from Vienna. He then visits various towns in Germany and after negotiations with the Philhellene professor Fr. Thiersch in Munich, he purchases large quantities of ammunition to be despatched to Greece. He finally arrives in Greece early in 1824, and takes part—together with his three brothers who were already fighting—in the struggle for the liberation of the common great fartheland
UAVINE-XAI: eXplainable AI-Based Spectral Band Selection for Vineyard Monitoring Using UAV Hyperspectral Data
No full textAn efficient spectral band selection trustworthy machine learning (ML) framework for vineyard monitoring from uncrewed aerial vehicle (UAV) hyperspectral data is introduced. The UAV, equipped with Specim AFX-10, is used to capture data beyond the visible spectrum within the 400–1000 nm wavelength range for a total of 224 bands. Popular supervised ML algorithms are utilized for detecting vegetation canopy in vineyards and distinguishing it from existing land uses, namely ground and shadow. Explainable AI results accompany those from ML to identify the most important bands, and understand the contribution of their reflectance levels to the ML models. By doing so, the number of spectral bands is narrowed while maintaining the granularity of the HS data. Experimental results on UAVINE, a publicly available dataset, demonstrate excelling classification performance of random forest (RF) with an overall accuracy of 97.06%, and with precision, recall, and F1-scores following accordingly. With the use of the computationally efficient Tree SHAP algorithm applied on the RF, the bands B106 (677 nm—Red), B186 (897 nm—NIR), B211 (967 nm—NIR), and B39 (498 nm—Green) were identified as the most important ones to the model, enabling better visualization of the vineyard and band-based analysis for each one of the classes existing within the vineyard
BauXAIte: An Explainable AI Framework for UAV Multisensor-Driven Bauxite Stockpile Mapping and Classification
No full textAn explainable artificial intelligence (AI) framework for bauxite stockpile classification through uncrewed aerial vehicle (UAV)-driven mapping, BauXAIte, is introduced for efficient mineral resource assessment and monitoring. A UAV, equipped with RGB and multispectral sensors, is used to capture data within a broad spectrum range, allowing the analysis of the bauxite stockpiles spectral signatures. Two distinct datasets, RGB and multispectral, were created by utilizing photogrammetric tools to generate the orthophoto map and GIS for annotating the bauxite types. These datasets along with their combination, were used to train a pool of machine and deep learning models to classify spectral signatures into bauxite types. Experimental results demonstrated that, using a multilayer perceptron (MLP), the classification performance exceeded 91% across all standard metrics. To ensure model transparency, Kernel SHapley Additive exPlanations (SHAP) results accompanied the AI predictions, to indicate correlations between spectral bands and classification decisions, thereby supporting end-users toward practical deployment of bauxite resource management
Measurement of damage growth in ultrasonic spot welded joint
No full textUltrasonic spot welding is a joining technique for thermoplastic composites with great potential regarding processing speed and cost. To investigate the damage tolerance and possible inherent damage arresting behavior of multi-spot welded joints, a technique is necessary to measure damage growth in the joints under cyclic loading. Visual inspection is not possible because the damage is not located on the outside surface and conventional techniques such as C-scan are not practical during a fatigue test because the specimen would have to be removed from the setup. This paper details a methodology for quantifying damage growth rates in singlespot welded joints using surface strain measurements made by Digital Image Correlation. This represents the first step towards developing a methodology for quantifying damage progression behavior in complex multi-spot welded joints.Structural Integrity & CompositesAerospace Structures & Computational Mechanic
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