SmartHerd Management: A Microservices Based Fog Computing Assisted IoT Platform towards Data Driven Smart Dairy Farming

Internet of things (IoT), fog computing, cloud computing and data driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing assisted end-to-end IoT platform for animal behaviour analysis and health monitoring in a dairy farming scenario. The platform follows a microservices oriented design to assist the distributed computing paradigm, and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6 month mature real-world deployment, wherein the data from wearables on cows is sent to a fog based platform for data classification and analysis, which includes decision making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog based computational assistance in the SmartHerd setup, we see an 84\% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

Airborne Vision-Based Remote Sensing Imagery Datasets From Large Farms Using Autonomous Drones For Monitoring Livestock

Livestock have high economic value and monitoring of them in large farms regularly is a labour-intensive task and costly. The emergence of smart data on individual animals and their surroundings opens up new opportunities for early detection and disease prevention, better animal care and traceability, better sustainability and farm economics. Precision Livestock Farming (PLF) relies on the constant and automated gathering of livestock data to support the expertise and management decisions made by farmers, vets, and authorities. The high mobility of UAVs combined with a high level of autonomy, sensor-driven technologies and AI decision-making abilities can provide many advantages to farmers in exploiting instant information from every corner of a large farm. The key objectives of this research are to i) explore various drone-mounted vision-based remote sensing modalities, particularly, visual band sensing and a thermal imager, ii) develop UAV-assisted autonomous PLF technologies and ii) collect data with various parameters for the researchers to establish further advanced AI-based approaches for monitoring livestock in large farms effectively by fusing a rich set of features acquired using vision-based multi-sensor modalities. The collected data suggest that the fuse of distinctive features of livestock obtained from multiple sensor modalities can be exploited to help farmers experience better livestock management in large farms through PLF

The effects of rubber matting and a novel gait analysis on growth performance and mobility of cattle in indoor feeding facilities

The objective for Chapter 2 was to determine effects of old and new rubber matting in a slatted, indoor cattle feeding facility on cattle growth performance, locomotion, and carcass characteristics. In experiment 1, fall-born Angus × Simmental steers (N = 207; BW = 222 ± 38 kg) were blocked by weight and assigned to 32 pens. Pens were randomly assigned to 1 of 4 treatments: no matting/concrete (CONC1), 12-year-old Animat Pebble matting (OLD1), new Animat Maxgrip matting (MG), and new Animat Pebble matting (PEB1). Steers were fed a common diet for 209 d with a minimum stocking density of 3.40 m2 per animal. Final body weight (BW) and average daily gain (ADG) were affected (P ≤ 0.02) by treatment with steers on PEB1 finishing heaviest with the greatest growth, MG and CONC1 intermediate, and OLD1 finishing at the lightest final BW with the least growth. Flooring treatment did not affect overall dry matter intake (DMI, P = 0.16) or gain to feed ratio (G:F, P = 0.94). Flooring treatment did not affect (P ≥ 0.19) any carcass traits. Locomotion scores (LS) were affected (P < 0.01) by flooring treatment with CONC1 having the worst mobility while OLD1, MG, and PEB1 were similar (P ≥ 0.24). Locomotion score had a day effect (P < 0.01) where cattle gait and mobility worsened as days on feed increased. In experiment 2, fall-born Angus × Simmental steers (N = 189; BW = 352 ± 43 kg) were blocked by weight and assigned to 21 pens. Pens were randomly assigned to 1 of 3 treatments: no matting/concrete (CONC2), 15-year-old Animat Pebble matting (OLD2), and new Animat Pebble matting (PEB2). Steers were fed a common diet for 112 d with a stocking density of 2.65 m2 per steer. After 112 days on feed, flooring treatment did not affect (P ≥ 0.30) BW, ADG, or DMI nor did treatment affect (P ≥ 0.17) carcass traits. However, steers housed on OLD2 or PEB2 had improved locomotion scores (P = 0.02) compared to steers housed on CONC2. Locomotion score had a day effect (P < 0.01) as cattle gait and mobility worsened with greater number of days on feed, regardless of treatment. Overall, results suggest new rubber matting increased ADG and HCW during a 209 d trial when cattle were stocked at 3.4 m2 and that rubber matting regardless of age improved cattle locomotion scores in slatted indoor feeding facilities. The objective for Chapter 3 was to determine the variance of locomotion score (LS) and growth performance attributable to flooring treatment, hind leg angle and step length (SL) measured by 3-D image analysis for cattle in slatted feeding facilities. Inherent individual differences in structural conformation may be related to cattle mobility and growth performance in indoor slatted facilities. Angus × Simmental steers (N = 189; BW = 352 ± 43 kg) were blocked by initial BW and assigned to 21 pens. Pens were randomly assigned to 1 of 3 treatments (TRT): concrete slats with no matting (CONC), 15-year-old Animat Pebble matting (OLD), and new Animat Pebble matting (PEB). Steers were fed for 152 days. Individual steers videos were recorded on d 0 using an Intel RealSense depth camera and processed using MATLAB to estimate hind leg angle, SL, and body length (BL). Locomotion scores were assigned using a 0 to 3 scale (Zinpro Step-Up® Locomotion Scoring System) throughout the finishing phase. The CORR procedure of SAS 9.4 was utilized to measure correlation of structural conformation traits to average LS, overall ADG, and final BW. Average LS had the greatest correlated (r = -0.23) to SL/BL during the finishing phase. The greatest correlation (r = -0.49) to overall ADG was average LS. Final BW had the strongest correlation (r = 0.51) to BL. The MIVQUE0 option of the MIXED procedure of SAS 9.4 was utilized to estimate the proportion of variance in average LS, overall ADG, and final BW. Variance of average LS was attributed to SL and SL × BL × TRT at 64% and 28%, respectively. For overall ADG, variance was attributable to SL × BL, SL × BL × TRT, and TRT at 38%, 35%, and 25%, respectively. Variables of SL, BL, SL × BL × TRT, and TRT accounted for 38%, 23%, 23%, and 15% of the variance in final BW, respectively. Overall, variance of average LS, overall ADG, and final BW were primarily attributed to SL, BL, TRT, and their interactions. Individual animal differences in structural conformation are related to cattle mobility and growth performance in slatted indoor facilities

The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review

The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019)