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

Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Crop and animal production techniques have changed significantly over the last century. In the early 1900s, animal power was replaced by tractor power that resulted in tremendous improvements in field productivity, which subsequently laid foundation for mechanized agriculture. While precision agriculture has enabled site-specific management of crop inputs for improved yields and quality, precision livestock farming has boosted efficiencies in animal and dairy industries. By 2020, highly automated systems are employed in crop and animal agriculture to increase input efficiency and agricultural output with reduced adverse impact on the environment. Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. This Issue Paper presents opportunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adoption. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training needs of the workforce who will be involved in the next-generation agriculture

An adaptive pig face recognition approach using convolutional neural networks

The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig. In the EU for instance, this capability is required for certification purposes. Although the RFID technology has been gradually developed and widely applied for this task, chip implanting might still be time-consuming and costly for current practical applications. In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition. Firstly, pig faces and eyes are detected automatically by two Haar feature-based cascade classifiers and one shallow convolutional neural network to extra high-quality images. Secondly, face recognition is performed by employing a deep convolutional neural network. Additionally, class activation maps generated by grad-CAM and saliency maps are utilised to visually understand how the discriminating parameters have been learned by the neural network. By applying the proposed approach on 10 randomly selected pigs filmed in farm condition, the proposed method demonstrates the superior performance against the state-of-art method with an accuracy of 83% over 320 testing images. The outcome of this study will facilitate the real-application of AI-based animal identification in swine production

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

Development of a mobile open-circuit system based on indirect calorimetry for energetic metabolism studies in small ruminants

Tesis por compendio[EN] For many years energy needs of ruminants have tried to be known to formulate rations adjusted, but it has been found that there are a variety of factors that affect them. Therefore, lots of studies are needed for evaluating the effect of these factors. Consequently, the main objective of this Thesis was to design and validate a respirometry system based on indirect calorimetry, which would allow assessing energy needs of small ruminants accurately. It was intended from the beginning it was a mobile system and of relatively low cost. Furthermore, a methane gas analyzer was incorporated to this system, which allowed the measurement of emissions of this greenhouse gas and quantification of energy losses in the form of methane. Initially the system had connected a mask, which was placed on the animal's face. A sample of exhaled gas was stored in a gas collection bag which was connected to the analyzer, and it measured the concentration of O2, CO2 and CH4 from the air. The proper functioning of the system was checked by a pilot experiment with dry Murciano-Granadina breed goats fed at maintenance level. Later this system was improved. Some of the most important changes were the replacement of the mask by a head hood in which the animal introduced the whole head, and the development of software that recorded and kept automatically concentrations of O2, CO2 and CH4 in exhaled air. This improvement allowed gas measurements during longer periods of time and recording more data. These changes were also validated through a pilot test with dry Manchega breed sheep. Subsequently, three experiments were performed. One of them with dry Guirra ewes and the other two with Murciano-Granadina goats during mid lactation. Diets were mixed rations that differed in the inclusion of cereal or fibrous by-products. In these experiments the effect of diet was studied on digestibility, energy balance and carbon-nitrogen, oxidation of nutrients, rumen parameters and methane production; in the case of lactating goats, also on milk performance. The determination of the calibration factor for O2 (1.005 ± 0.0101) confirmed the proper functioning of equipment. Moreover, small differences between the heat production obtained by indirect calorimetry and the carbon-nitrogen balance (2% in sheep and 1% in goats) demonstrated that this system allows determining the heat production of the animals reliably and accurately. In the experiments of this Thesis have been estimated maintenance energy needs of two Spanish native sheep breeds, such as the sheep from the Guirra and Manchega breeds; net maintenance requirements were 270 kJ/kg BW0.75, on average. In the case of Murciano-Granadina breed goats, in the middle of lactation, the average utilization efficiency of metabolizable energy for lactation was 0.61.[ES] Desde hace años se ha tratado de conocer las necesidades energéticas de los rumiantes con el fin de formular raciones ajustadas, pero se ha comprobado que hay una gran variedad de factores que les afectan; por ello son necesarios estudios que evalúen el efecto de estos factores. Como consecuencia, el principal objetivo de esta tesis fue diseñar y validar un equipo de respirometría, basado en calorimetría indirecta, que permitiese evaluar las necesidades en energía de pequeños rumiantes de forma precisa. Se pretendió desde el inicio que fuese un sistema móvil y de relativo bajo coste. Además, a este sistema también se le incorporó un analizador de gas metano, que permitía la medición de las emisiones de este gas de efecto invernadero y la cuantificación de las pérdidas energéticas en forma de metano. Inicialmente el equipo tenía conectada una máscara que se colocaba en la cara del animal. Una muestra del gas espirado era almacenada en una bolsa de recogida de gases que era conectada al analizador, el cual medía la concentración de O2, CO2 y CH4 del aire. Se comprobó el correcto funcionamiento del sistema mediante una prueba piloto con cabras de raza Murciano-Granadina secas, alimentadas a nivel de mantenimiento. Posteriormente este sistema fue mejorado. Algunos de los cambios más importantes fueron la sustitución de la máscara por una urna en la que el animal introducía la cabeza entera, y el desarrollo de un software que registraba y guardaba de forma automática las concentraciones de O2, CO2 y CH4 del aire espirado. Esta mejora permitía medidas de gases durante periodos de tiempo más largos y el registro de muchos más datos. Estas modificaciones también fueron validadas mediante una prueba piloto con ovejas de raza Manchega secas. Posteriormente se realizaron tres experimentos. Uno de ellos con ovejas de raza Guirra secas y los otros dos con cabras Murciano-Granadinas en mitad de lactación. Las dietas fueron raciones mixtas que diferían en la inclusión de cereal o subproductos fibrosos. En estos experimentos se estudió el efecto de la dieta sobre la digestibilidad, balances de energía y carbono-nitrógeno, oxidación de los nutrientes, parámetros del rumen y producción de metano; en el caso de las cabras en lactación, también sobre los rendimientos productivos. La determinación del factor de calibrado para el O2 (1,005 ± 0,0101) confirmó el buen funcionamiento del equipo. Por otro lado, las pequeñas diferencias entre la producción de calor obtenida mediante calorimetría indirecta y el balance de carbono-nitrógeno (2% en ovejas y 1% en cabras) demostraron que este sistema permite determinar la producción de calor de los animales de forma fiable y precisa. En los trabajos de esta Tesis se han estimado las necesidades energéticas de mantenimiento en dos razas de ovejas autóctonas españolas, como son las razas Guirra y Manchega; las necesidades netas de mantenimiento fueron 270 kJ/kg PV0,75, de media. En el caso del ganado caprino de raza Murciano-Granadina, en mitad de lactación, la eficacia media de utilización de la energía metabolizable para la lactación fue de 0,61.[CA] Des de fa anys s'ha tractat de conèixer les necessitats energètiques dels remugants a fi de formular racions ajustades, però s'ha comprovat que hi ha una gran varietat de factors que els afecten; per això són necessaris estudis que avaluen l'efecte d'estos factors. Com a conseqüència, el principal objectiu d'aquesta Tesi va ser dissenyar i validar un equip de respirometría, basat en calorimetria indirecta, que permetera avaluar les necessitats en energia de menuts remugants de forma precisa. Es va pretendre des de l'inici que fóra un sistema mòbil i de relatiu baix cost. A més, a este sistema també se li va incorporar un analitzador de gas metà, que permetia el mesurament de les emissions d'este gas d'efecte hivernacle i la quantificació de les pèrdues energètiques en forma de metà. Inicialment l'equip tenia connectada una màscara que es col·locava en la cara de l'animal. Una mostra del gas expirat era emmagatzemada en una bossa d'arreplega de gasos que era connectada a l'analitzador, el qual mesurava la concentració d'O2, CO2 i CH4 de l'aire. Es va comprovar el funcionament correcte del sistema per mitjà d'una prova pilot amb cabres de raça Murciano-Granadina seques, alimentades a nivell de manteniment. Posteriorment este sistema va ser millorat. Alguns dels canvis més importants van ser la substitució de la màscara per una urna en què l'animal introduïa el cap sencera, i el desenrotllament d'un programari que registrava i guardava de forma automàtica les concentracions d'O2, CO2 i CH4 de l'aire expirat. Esta millora permetia mesures de gasos durant períodes de temps més llargs i el registre de moltes més dades. Estes modificacions també van ser validades per mitjà d'una prova pilot amb ovelles de raça Manxega seques. Després es van realitzar tres experiments. Un d'ells amb ovelles de raça Guirra seques i els altres dos amb cabres Murciano-Granadinas en mitat de lactació. Les dietes van ser racions mixtes que diferien en la inclusió de cereal o subproductes fibrosos. En estos experiments es va estudiar l'efecte de la dieta sobre la digestibilitat, balanços d'energia i carboni-nitrogen, oxidació dels nutrients, paràmetres del rumen i producció de metà; en el cas de les cabres en lactació, també sobre els rendiments productius. La determinació del factor de calibrat per a l'O2 (1,005 ± 0,0101) va confirmar el bon funcionament de l'equip. D'altra banda, les xicotetes diferències entre la producció de calor obtinguda per mitjà de calorimetria indirecta i el balanç de carboni-nitrogen (2% en ovelles i 1% en cabres) van demostrar que este sistema permet determinar la producció de calor dels animals de forma fiable i precisa. En els treballs d'esta Tesi s'han estimat les necessitats energètiques de manteniment en dos races d'ovelles autòctones espanyoles, com són les races Guirra i Manxega; les necessitats netes de manteniment van ser 270 kJ/kg PV0,75, de mitja. En el cas del bestiar caprí de raça Murciano-Granadina, en mitat de lactació, l'eficàcia mitjana d'utilització de l'energia metabolitzable per a la lactació va ser de 0,61.López Luján, MDC. (2015). Development of a mobile open-circuit system based on indirect calorimetry for energetic metabolism studies in small ruminants [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/50430TESISCompendi