Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

4.Uluslararası Öğrenciler Fen Bilimleri Kongresi Bildiriler Kitabı

Çevrimiçi ( XIII, 495 Sayfa ; 26 cm.)

Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep

Lameness can be described as painful erratic movements, which relate to a locomotor system and result in the animal deviating from its normal gait or posture. Lameness is considered one of the major health and welfare concerns for the sheep industry in the UK that leads to a substantial economic problem and causes a reduction in overall farm productivity. According to a report in 2013 by ADAS entitled ‘Economic Impact of Health and Welfare Issues in Beef, Cattle and Sheep in England’, each lame ewe costs £89.80 due to the decline in body condition, lambing percentage, growth rate, and reduced fertility. Thus, early lameness detection eliminates the negative impact of lameness and increase the chance of favourable outcome from treatment. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal behaviours or movements which relate to lameness. The aim of this thesis was to evaluate the feasibility and accessibility of a proposed data mining approach (SLDM) to detect the early signs of lameness in sheep via analysing the retrieved data from a mounted wearable motion sensor within a sheep’s neck collar through investigating the most cost effective factors that contribute to lameness detection within the whole data mining process including; sensor sampling rate, segmentation methods, window size, extracted features, feature selection methods, and applicable classification algorithm. Three classes are recognised for sheep while their walking throughout the data collection process (sound, mild, and severe lameness classes). The sheep data were collected using three different sensor applications (Sheep Tracker, SensoDuino, SensorLog) which collect sheep data movements at different sampling rates 10, 5, and 4 Hz. Various sensing data were retrieved in X,Y, and Z dimensions; however, only accelerometer, gyroscope, and orientation readings are considered in the current study. Four sheep datasets are aggregated each of which includes 31, 10, 18, and 7 sheep. The conducted work in this thesis evaluates the performance of ensemble classifiers (Bagging, Boosting, or RusBoosting) using three different validation methods (5-fold, 0.3 hold-out, and proposed one ‘Single Sheep Splitting’) in comparison to three sampling rates (10, 5, 4 Hz), two segmentation approaches (FNSW and FOSW), three feature selection methods (ReliefF, GA, and RF) and three window sizes (10, 7, 5 sec.). Promising results of lameness prediction accuracies are achieved over most of the combinations (3 sampling rates, two segmentation methods, 3 window sizes, 183 extracted features, 3 feature selection methods, 3 ensemble classification models, and 3 model validation methods). However, the highest accuracy is revealed by using the `Bagging ensemble classifier 88.92% with F-score of 87.7%, 91.1%, 88.2% for sound walking, mildly walking, and severely walking classes, respectively. The results are obtained using 5-fold cross-validation over a 10 sec.window for sheep data collected at 10 Hz sampling rate using only the accelerometer hardware sensor reading and calculated orientation readings. The number of features selected is 46 optimised by GA using CHAID tree as a fitness function. Conversely, the lowest prediction accuracy of 56.25% with F-score (63.4% sound walking, 51.9% mildly walking, 48.8% severely walking) is recorded when RusBoosting ensemble is applied using 5-fold cross-validation over a 10 sec.window for dataset collected at the 4 Hz. sampling rate. So, the major research findings recommend that 10 Hz sampling rate is adequate for collect sheep movements, while the best segmentation method is FOSW as 20% of data-points are shared between two successive windows. Whereas, the preferable number of data-points (sheep movements) to be pre-processed is around 100, which is obtained when a 10 sec.window size or 7 sec.window size is applied. Additionally, the 20 features selected by RF out of 183 features could reveal good accuracy results compared to the whole set of extracted features. Although that GA feature selection method has slower execution time than RF, competitive prediction accuracy could be achieved when the selected features by GA were fed to the classifier. Finally, the acceleration sensor data alone are capable of making the decision about the lame sheep. So no extra hardware sensors like Gyroscope is required for decision making; moreover, the orientation sensor features could be directly derived from Acc which contribute most to lameness detection. Since the most cost effective factors are identified in this research, the practice in the meanwhile could be applicable for farmers, stakeholders, and manufacturers as no available sensor to detect the lame sheep developed yet. Therefore, the multidisciplinary nature of the conducted research opens diverse paths towards applying further research studies to develop various data mining approaches and practical sensor kits to detect the early signs of sheep’s lameness for better farm productivity and sheep industry prosperity in the UK

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Outils statistiques pour la sélection de variables\ud et l'intégration de données "omiques"

Les récentes avancées biotechnologiques permettent maintenant de mesurer une\ud énorme quantité de données biologiques de différentes sources (données génomiques,\ud protémiques, métabolomiques, phénotypiques), souvent caractérisées par un petit nombre\ud d'échantillons ou d'observations.\ud L'objectif de ce travail est de développer ou d'adapter des méthodes statistiques\ud adéquates permettant d'analyser ces jeux de données de grande dimension, en proposant\ud aux biologistes des outils efficaces pour sélectionner les variables les plus pertinentes.\ud Dans un premier temps, nous nous intéressons spécifiquement aux données de\ud transcriptome et à la sélection de gènes discriminants dans un cadre de classification\ud supervisée. Puis, dans un autre contexte, nous cherchons a sélectionner des variables de\ud types différents lors de la réconciliation (ou l'intégration) de deux tableaux de données\ud omiques.\ud Dans la première partie de ce travail, nous proposons une approche de type\ud wrapper en agrégeant des méthodes de classification (CART, SVM) pour sélectionner\ud des gènes discriminants une ou plusieurs conditions biologiques. Dans la deuxième\ud partie, nous développons une approche PLS avec pénalisation l1 dite de type sparse\ud car conduisant à un ensemble "creux" de paramètres, permettant de sélectionner des\ud sous-ensembles de variables conjointement mesurées sur les mêmes échantillons biologiques.\ud Un cadre de régression, ou d'analyse canonique est propose pour répondre\ud spécifiquement a la question biologique.\ud Nous évaluons chacune des approches proposées en les comparant sur de nombreux\ud jeux de données réels a des méthodes similaires proposées dans la littérature.\ud Les critères statistiques usuels que nous appliquons sont souvent limitée par le petit\ud nombre d'échantillons. Par conséquent, nous nous efforcons de toujours combiner nos\ud évaluations statistiques avec une interprétation biologique détaillee des résultats.\ud Les approches que nous proposons sont facilement applicables et donnent des\ud résultats très satisfaisants qui répondent aux attentes des biologistes.------------------------------------------------------------------------------------Recent advances in biotechnology allow the monitoring of large quantities of\ud biological data of various types, such as genomics, proteomics, metabolomics, phenotypes...,\ud that are often characterized by a small number of samples or observations.\ud The aim of this thesis was to develop, or adapt, appropriate statistical methodologies\ud to analyse highly dimensional data, and to present ecient tools to biologists\ud for selecting the most biologically relevant variables. In the rst part, we focus on\ud microarray data in a classication framework, and on the selection of discriminative\ud genes. In the second part, in the context of data integration, we focus on the selection\ud of dierent types of variables with two-block omics data.\ud Firstly, we propose a wrapper method, which agregates two classiers (CART\ud or SVM) to select discriminative genes for binary or multiclass biological conditions.\ud Secondly, we develop a PLS variant called sparse PLS that adapts l1 penalization and\ud allows for the selection of a subset of variables, which are measured from the same\ud biological samples. Either a regression or canonical analysis frameworks are proposed\ud to answer biological questions correctly.\ud We assess each of the proposed approaches by comparing them to similar methods\ud known in the literature on numerous real data sets. The statistical criteria that\ud we use are often limited by the small number of samples. We always try, therefore, to\ud combine statistical assessments with a thorough biological interpretation of the results.\ud The approaches that we propose are easy to apply and give relevant results that\ud answer the biologists needs