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A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 68721

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Special Issue Editors

Dr. Pedro Gonçalves

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Guest Editor

Technology and Management School of Águeda, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: internet of things; network management; system administration; smart farm; animal monitoring
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Dr. Paulo Pedreiras

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Guest Editor

Electronics, Telecommunications and Informatics Department, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: distributed real-time systems; industrial communications; real-time scheduling; real-time medium access control; dynamic quality-of-service management; industrial internet of things; cyber–physical systems
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Prof. Dr. António Monteiro

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Guest Editor

Research Centre for Natural Resources, Environment and Society (CERNAS), Escola Superior Agrária, Instituto Politécnico de Viseu, P3500-606 Viseu, Portugal
Interests: small ruminant production; precision agriculture; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 concept has mostly been translated into the use of sensor networks, which, when interconnected with cloud-hosted applications, allow for the management of the entire industrial value chain in an integrated and more efficient way. In the last decade the “4.0” concept has been adapted to the most diverse areas, including agriculture. Its core objective remains the same, consisting of the optimization of production processes by using ICT technologies to sense the most diverse aspects of the production environment and analyze the gathered data, in order to support the management decision process and the acquisition of in-depth knowledge of the production chain’s state. Agriculture 4.0, or Smart Farming, is the application of these “4.0” technologies to the farming sector, which is a scientific and engineering domain that is gaining noticeable momentum.

Currently, the term “5.0” is being used, both in an industrial and agricultural context, and it is believed that this trend should include the use of robots to perform autonomous tasks, and big data-based processes.

This Special Issue aims to highlight the latest research results and advances in technologies relevant to the automation of agriculture and farming processes, commonly known as Smart Farming/Agriculture 4.0/Agriculture 5.0; therefore, we welcome the submission of original papers presenting significant advances with respect to the state of the art, featuring a solid theoretical development and practical relevance.

Topics of interest falling under the scope of Smart Farming/Agriculture 4.0/Agriculture 5.0 include, but are not limited to: (i) autonomous farming processes, (ii) big data-based processes for agriculture or animal handling.

Prof. Dr. Pedro Gonçalves
Prof. Dr. Paulo Pedreiras
Prof. Dr. António Monteiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart farm
animal monitoring
machine learning
animal location tracking
wellbeing monitoring

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

3 pages, 197 KiB

Open AccessEditorial

Recent Advances in Smart Farming

by Pedro Gonçalves, Paulo Pedreiras and António Monteiro

Animals 2022, 12(6), 705; https://doi.org/10.3390/ani12060705 - 11 Mar 2022

Cited by 5 | Viewed by 2685

Abstract

The Digital Transformation, which has the Internet of Things (IoT) as one of its pillars, is penetrating all aspects of our society with dramatic effects [...] Full article

(This article belongs to the Special Issue Smart Farm)

Research

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28 pages, 3313 KiB

Open AccessArticle

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

by Anniek Eerdekens, Margot Deruyck, Jaron Fontaine, Bert Damiaans, Luc Martens, Eli De Poorter, Jan Govaere, David Plets and Wout Joseph

Animals 2021, 11(10), 2904; https://doi.org/10.3390/ani11102904 - 7 Oct 2021

Cited by 5 | Viewed by 4312

Abstract

Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively. Full article

(This article belongs to the Special Issue Smart Farm)

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30 pages, 3748 KiB

Open AccessArticle

Design of Scalable IoT Architecture Based on AWS for Smart Livestock

by Kristina Dineva and Tatiana Atanasova

Animals 2021, 11(9), 2697; https://doi.org/10.3390/ani11092697 - 15 Sep 2021

Cited by 33 | Viewed by 8873

Abstract

In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behaviour, reproduction, emotional state, and stress levels of animals. The AWS services used, and their specific tasks related to the proposed architecture are explained in detail. A stress test was performed to prove the data ingesting and processing capability of the proposed architecture. Experimental results proved that the proposed architecture using AWS automated scaling mechanisms and IoT devices are fully capable of processing the growing amount of data, which in turn allow for meeting the required needs of the constantly expanding number of CPS systems. Full article

(This article belongs to the Special Issue Smart Farm)

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21 pages, 5433 KiB

Open AccessArticle

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

by Pedro Gonçalves, Luís Nóbrega, António Monteiro, Paulo Pedreiras, Pedro Rodrigues and Fernando Esteves

Animals 2021, 11(9), 2625; https://doi.org/10.3390/ani11092625 - 7 Sep 2021

Cited by 14 | Viewed by 3747

Abstract

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. Full article

(This article belongs to the Special Issue Smart Farm)

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15 pages, 1707 KiB

Open AccessArticle

A Novel Miniaturized Biosensor for Monitoring Atlantic Salmon Swimming Activity and Respiratory Frequency

by Jelena Kolarevic, Josep Calduch-Giner, Åsa M. Espmark, Tor Evensen, Javier Sosa and Jaume Pérez-Sánchez

Animals 2021, 11(8), 2403; https://doi.org/10.3390/ani11082403 - 14 Aug 2021

Cited by 10 | Viewed by 4288

Abstract

The advanced development of sensor technologies has led to the emergence of fish biosensors that are currently used for research and commercial purposes. AEFishBIT is a miniaturized biosensor attached to fish operculum that measures physical activity and respiration frequencies. In this study, we determined the effect of the tagging method and evaluated the use of this biosensor to monitor post-smolt Atlantic salmon in a tank-based system. The use of piercing fish tag had a negative impact on the gills and operculum, unlike the identical protocols used in gilthead sea bream and European sea bass. In contrast, a surgical thread did not show any apparent tissue damage. Two data recording schedules were considered to monitor immediate early reaction to fish handling and light regime changes (records every 15 min over 2 days) or adaptation to new light conditions (records every 30 min over 4 days). Data showed stabilization of physical activity 8 h post-tagging, with different steady states for the activity/respiratory ratio after changes in light intensity that reflected a different time course adaptation to new light conditions. High correlations were observed between AEFishBIT and video recording data. These findings supported the use of AEFishBIT as a promising tool for smart sensing of Atlantic salmon. Full article

(This article belongs to the Special Issue Smart Farm)

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9 pages, 1108 KiB

Open AccessArticle

Calf Birth Weight Predicted Remotely Using Automated in-Paddock Weighing Technology

by Anita Z. Chang, José A. Imaz and Luciano A. González

Animals 2021, 11(5), 1254; https://doi.org/10.3390/ani11051254 - 27 Apr 2021

Cited by 5 | Viewed by 2439

Abstract

The present study aimed to develop predictive models of calf birth weight (CBW) from liveweight (LW) data collected remotely and individually using an automated in-paddock walk-over-weighing scale (WOW). Twenty-eight multiparous Charolais cows were mated with two Brahman bulls. The WOW was installed at the only watering point to capture LW over five months. Calf birth date and weight were manually recorded, and the liveweight change experienced by a dam at calving (ΔLWC) was calculated as pre-LW minus post-LW calving. Cow non-foetal weight loss at calving (NFW) was calculated as ΔLWC minus CBW. Pearson’s correlational analysis and simple linear regressions were used to identify associations between all variables measured. No correlations were found between ΔLWC and pre-LW (p = 0.52), or post-LW (p = 0.14). However, positive associations were observed between ΔLWC and CBW (p < 0.001, R2 = 0.56) and NFW (p < 0.001, R2 = 0.90). Thus, the results suggest that 56% of the variation in ΔLWC is attributed to the calf weight, and consequently could be used as an indicator of CBW. Remote, in-paddock weighing systems have the potential to provide timely and accurate LW data of breeding cows to improve calving management and productivity. Full article

(This article belongs to the Special Issue Smart Farm)

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24 pages, 9729 KiB

Open AccessArticle

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

by Elanchezhian Arulmozhi, Jayanta Kumar Basak, Thavisack Sihalath, Jaesung Park, Hyeon Tae Kim and Byeong Eun Moon

Animals 2021, 11(1), 222; https://doi.org/10.3390/ani11010222 - 18 Jan 2021

Cited by 45 | Viewed by 4945

Abstract

Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R² = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R² = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes. Full article

(This article belongs to the Special Issue Smart Farm)

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19 pages, 14715 KiB

Open AccessArticle

Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting

by Jennifer Salau and Joachim Krieter

Animals 2020, 10(12), 2402; https://doi.org/10.3390/ani10122402 - 15 Dec 2020

Cited by 25 | Viewed by 4161

Abstract

With increasing herd sizes came an enhanced requirement for automated systems to support the farmers in the monitoring of the health and welfare status of their livestock. Cattle are a highly sociable species, and the herd structure has important impact on the animal welfare. As the behaviour of the animals and their social interactions can be influenced by the presence of a human observer, a camera based system that automatically detects the animals would be beneficial to analyse dairy cattle herd activity. In the present study, eight surveillance cameras were mounted above the barn area of a group of thirty-six lactating Holstein Friesian dairy cows at the Chamber of Agriculture in Futterkamp in Northern Germany. With Mask R-CNN, a state-of-the-art model of convolutional neural networks was trained to determine pixel level segmentation masks for the cows in the video material. The model was pre-trained on the Microsoft common objects in the context data set, and transfer learning was carried out on annotated image material from the recordings as training data set. In addition, the relationship between the size of the used training data set and the performance on the model after transfer learning was analysed. The trained model achieved averaged precision (Intersection over union, IOU = 0.5) 91% and 85% for the detection of bounding boxes and segmentation masks of the cows, respectively, thereby laying a solid technical basis for an automated analysis of herd activity and the use of resources in loose-housing. Full article

(This article belongs to the Special Issue Smart Farm)

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12 pages, 807 KiB

Open AccessArticle

Influence of Subclinical Ketosis in Dairy Cows on Ingestive-Related Behaviours Registered with a Real-Time System

by Ramūnas Antanaitis, Vida Juozaitienė, Mindaugas Televičius, Dovilė Malašauskienė, Mingaudas Urbutis and Walter Baumgartner

Animals 2020, 10(12), 2288; https://doi.org/10.3390/ani10122288 - 3 Dec 2020

Cited by 7 | Viewed by 2728

Abstract

According to the literature, rumination time can be used as biomarker in the diagnosis of subclinical ketosis (SCK). We hypothesized that SCK in cows influences ingestive-related behaviours registered with the real-time system. The aim of the current study was to determine the influence of SCK on dairy cows’ ingestive-related behaviours registered with a real-time system. Twenty Lithuanian Black and White breed dairy cows were selected based on the following criteria: First day after calving, having two or more lactations (on average 3.0 ± 0.13 lactations), and being clinically healthy. The experiment lasted 18 days. Cows were tested 24 h a day for 17.5 days. On the day of diagnosis (day 0), data were recorded for 12 h. During the experimental period, one cow was studied for a total of 420 h. For the registration of rumination behaviour, the RumiWatch system (RWS) was used. It was found that cows with SCK showed lesser average values for the following parameters: rumination time and rumination chews (1.48 and 1.68 times respectively; p < 0.001), drinking time (1.50 times; p < 0.001), chews per minute, bolus and chews per bolus (1.12, 1.45 and 1.51 times; p < 0.001). From the 15th day before the diagnosis of SCK, rumination time in health cows was greater than that in SCK cows from −0.96% (−17 day) to 187.79% (0 days, < 0.001). We estimated the greater average value of drinking time in healthy cows compared with SCK cows from 34.22% on day −17 to −121.67% on day 0 (p < 0.001). Decrease in rumination time was associated with a significant increase in the probability of risk of SCK. Further studies are needed with a larger number of cows with SCK. Full article

(This article belongs to the Special Issue Smart Farm)

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Review

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31 pages, 6713 KiB

Open AccessReview

Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

by Mingyung Lee and Seongwon Seo

Animals 2021, 11(10), 2779; https://doi.org/10.3390/ani11102779 - 23 Sep 2021

Cited by 43 | Viewed by 6830

Abstract

The review aimed to collect information about the wearable wireless sensor system (WWSS) for cattle and to conduct a systematic literature review on the accuracy of predicting the physiological parameters of these systems. The WWSS was categorized as an ear tag, halter, neck collar, rumen bolus, leg tag, tail-mounted, and vaginal mounted types. Information was collected from a web-based search on Google, then manually curated. We found about 60 WWSSs available in the market; most sensors included an accelerometer. The literature evaluating the WWSS performance was collected through a keyword search in Scopus. Among the 1875 articles identified, 46 documents that met our criteria were selected for further meta-analysis. Meta-analysis was conducted on the performance values (e.g., correlation, sensitivity, and specificity) for physiological parameters (e.g., feeding, activity, and rumen conditions). The WWSS showed high performance in most parameters, although some parameters (e.g., drinking time) need to be improved, and considerable heterogeneity of performance levels was observed under various conditions (average I² = 76%). Nevertheless, some of the literature provided insufficient information on evaluation criteria, including experimental conditions and gold standards, to confirm the reliability of the reported performance. Therefore, guidelines for the evaluation criteria for studies evaluating WWSS performance should be drawn up. Full article

(This article belongs to the Special Issue Smart Farm)

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18 pages, 352 KiB

Open AccessReview

Precision Agriculture for Crop and Livestock Farming—Brief Review

by António Monteiro, Sérgio Santos and Pedro Gonçalves

Animals 2021, 11(8), 2345; https://doi.org/10.3390/ani11082345 - 9 Aug 2021

Cited by 130 | Viewed by 20642

Abstract

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. Full article

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