Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios

Automatic animal monitoring can bring several advantages to the livestock sector. The emergence of low-cost and low-power miniaturized sensors, together with the ability of handling huge amounts of data, has led to a boost of new intelligent farming solutions. One example is the SheepIT solution that is being commercialized by iFarmtec. The main objectives of the solution are monitoring the sheep’s posture while grazing in vineyards, and conditioning their behaviour using appropriate stimuli, such that they only feed from the ground or from the lower branches of the vines. The quality of the monitoring procedure has a linear correlation with the animal condition capability of the solution, i.e., on the effectiveness of the applied stimuli. Thus, a Real-Time mechanism capable of identifying animal behaviour such as infraction, eating, walking or running movements and standing position is required. On a previous work we proposed a solution based on low-power microcontrollers enclosed in collars wearable by sheep. Machine Learning techniques have been rising as a useful tool for dealing with big amounts of data. From the wide range of techniques available, the use of Decision Trees is particularly relevant since it allows the retrieval of a set of conditions easily transformed in lightweight machine code. The goal of this paper is to evaluate an enhanced animal monitoring mechanism and compare it to existing ones. In order to achieve this goal, a real deployment scenario was availed to gather relevant data from sheep’s collar. After this step, we evaluated the impact of several feature transformations and pre-processing techniques on the model learned from the system. Due to the natural behaviour of sheep, which spend most of the time grazing, several pre-processing techniques were tested to deal with the unbalanced dataset, particularly resorting on features related with stateful history. Albeit presenting promising results, with accuracy over 96%, these features resulted in unfeasible implementations. Hence, the best feasible model was achieved with 10 features obtained from the sensors’ measurements plus an additional temporal feature. The global accuracy attained was above 91%. Howbeit, further research shall assess a way of dealing with this kind of unbalanced datasets and take advantage of the insights given by the results achieved when using the state’s history.publishe

Tecnologias IoT para pastoreio e controlo de postura animal

The unwanted and adverse weeds that are constantly growing in vineyards, force wine producers to repeatedly remove them through the use of mechanical and chemical methods. These methods include machinery such as plows and brushcutters, and chemicals as herbicides to remove and prevent the growth of weeds both in the inter-row and under-vine areas. Nonetheless, such methods are considered very aggressive for vines, and, in the second case, harmful for the public health, since chemicals may remain in the environment and hence contaminate water lines. Moreover, such processes have to be repeated over the year, making it extremely expensive and toilsome. Using animals, usually ovines, is an ancient practice used around the world. Animals, grazing in vineyards, feed from the unwanted weeds and fertilize the soil, in an inexpensive, ecological and sustainable way. However, sheep may be dangerous to vines since they tend to feed on grapes and on the lower branches of the vines, which causes enormous production losses. To overcome that issue, sheep were traditionally used to weed vineyards only before the beginning of the growth cycle of grapevines, thus still requiring the use of mechanical and/or chemical methods during the remainder of the production cycle. To mitigate the problems above, a new technological solution was investigated under the scope of the SheepIT project and developed in the scope of this thesis. The system monitors sheep during grazing periods on vineyards and implements a posture control mechanism to instruct them to feed only from the undesired weeds. This mechanism is based on an IoT architecture, being designed to be compact and energy efficient, allowing it to be carried by sheep while attaining an autonomy of weeks. In this context, the thesis herein sustained states that it is possible to design an IoT-based system capable of monitoring and conditioning sheep’s posture, enabling a safe weeding process in vineyards. Moreover, we support such thesis in three main pillars that match the main contributions of this work and that are duly explored and validated, namely: the IoT architecture design and required communications, a posture control mechanism and the support for a low-cost and low-power localization mechanism. The system architecture is validated mainly in simulation context while the posture control mechanism is validated both in simulations and field experiments. Furthermore, we demonstrate the feasibility of the system and the contribution of this work towards the first commercial version of the system.O constante crescimento de ervas infestantes obriga os produtores a manter um processo contínuo de remoção das mesmas com recurso a mecanismos mecânicos e/ou químicos. Entre os mais populares, destacam-se o uso de arados e roçadores no primeiro grupo, e o uso de herbicidas no segundo grupo. No entanto, estes mecanismos são considerados agressivos para as videiras, assim como no segundo caso perigosos para a saúde pública, visto que os químicos podem permanecer no ambiente, contaminando frutos e linhas de água. Adicionalmente, estes processos são caros e exigem mão de obra que escasseia nos dias de hoje, agravado pela necessidade destes processos necessitarem de serem repetidos mais do que uma vez ao longo do ano. O uso de animais, particularmente ovelhas, para controlar o crescimento de infestantes é uma prática ancestral usada em todo o mundo. As ovelhas, enquanto pastam, controlam o crescimento das ervas infestantes, ao mesmo tempo que fertilizam o solo de forma gratuita, ecológica e sustentável. Não obstante, este método foi sendo abandonado visto que os animais também se alimentam da rama, rebentos e frutos da videira, provocando naturais estragos e prejuízos produtivos. Para mitigar este problema, uma nova solução baseada em tecnologias de Internet das Coisas é proposta no âmbito do projeto SheepIT, cuja espinha dorsal foi construída no âmbito desta tese. O sistema monitoriza as ovelhas enquanto estas pastoreiam nas vinhas, e implementam um mecanismo de controlo de postura que condiciona o seu comportamento de forma a que se alimentem apenas das ervas infestantes. O sistema foi incorporado numa infraestrutura de Internet das Coisas com comunicações sem fios de baixo consumo para recolha de dados e que permite semanas de autonomia, mantendo os dispositivos com um tamanho adequado aos animais. Neste contexto, a tese suportada neste trabalho defende que é possível projetar uma sistema baseado em tecnologias de Internet das Coisas, capaz de monitorizar e condicionar a postura de ovelhas, permitindo que estas pastem em vinhas sem comprometer as videiras e as uvas. A tese é suportada em três pilares fundamentais que se refletem nos principais contributos do trabalho, particularmente: a arquitetura do sistema e respetivo sistema de comunicações; o mecanismo de controlo de postura; e o suporte para implementação de um sistema de localização de baixo custo e baixo consumo energético. A arquitetura é validada em contexto de simulação, e o mecanismo de controlo de postura em contexto de simulação e de experiências em campo. É também demonstrado o funcionamento do sistema e o contributo deste trabalho para a conceção da primeira versão comercial do sistema.Programa Doutoral em Informátic

Goat Kidding Dataset

The detection of kidding in production animals is of the utmost importance, given the frequency of problems associated with the process, and the fact that timely human help can be a safeguard for the well-being of the mother and kid. The continuous human monitoring of the process is expensive, given the uncertainty of when it will occur, so the establishment of an autonomous mechanism that does so would allow calling the human responsible who could intervene at the opportune moment. The present dataset consists of data from the sensorization of 16 pregnant and two non-pregnant Charnequeira goats, during a period of four weeks, the kidding period. The data include measurements from neck to floor height, measured by ultrasound and accelerometry data measured by an accelerometer existing at the monitoring collar. Data was continuously sampled throughout the experiment every 10 s. The goats were monitored both in the goat shelter (day and night) and during the grazing period in the pasture. The births of the animals were also registered, both in terms of the time at which they took place, but also with details regarding how they took place and the number of offspring, and notes were also added.info:eu-repo/semantics/publishedVersio

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

The use of animal sensor data for predicting sheep metabolisable energy intake using machine learning

The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems. The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake (MEI) of sheep by using temperature, pitch angle, roll angle, distance, speed, and grazing time data obtained directly from wearable sensors on the sheep. A Deep Belief Network (DBN) algorithm was used to predict MEI, which to our knowledge, has not been attempted previously. The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone. The mean square error (MSE) values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets, respectively. We also evaluated the influential sensor data variables, i.e., distance and pitch angle, for predicting the MEI. Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data. We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management, as wearable sensors become widely used by livestock producers

SheepIT, an E-Shepherd System for Weed Control in Vineyards: Experimental Results and Lessons Learned

Weed control in vineyards demands regular interventions that currently consist of the use of machinery, such as plows and brush-cutters, and the application of herbicides. These methods have several drawbacks, including cost, chemical pollution, and the emission of greenhouse gases. The use of animals to weed vineyards, usually ovines, is an ancestral, environmentally friendly, and sustainable practice that was abandoned because of the scarcity and cost of shepherds, which were essential for preventing animals from damaging the vines and grapes. The SheepIT project was developed to automate the role of human shepherds, by monitoring and conditioning the behaviour of grazing animals. Additionally, the data collected in real-time can be used for improving the efficiency of the whole process, e.g., by detecting abnormal situations such as health conditions or attacks and manage the weeding areas. This paper presents a comprehensive set of field-test results, obtained with the SheepIT infrastructure, addressing several dimensions, from the animals’ well-being and their impact on the cultures, to technical aspects, such as system autonomy. The results show that the core objectives of the project have been attained and that it is feasible to use this system, at an industrial scale, in vineyards.info:eu-repo/semantics/publishedVersio

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy