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Brief overview of project, main aim of project, potential findings and conclusions

Presents general project area, relevance of project, specific investigation of project and a brief outline of proposal to follow

Critically reviews existing work, identifies relevant research areas and any opposing views. Links to the gap your project will fill Provides precise and measurable research question/s that the project aim will answer. States project aim and steps to meet aim. Presents tangible sub-goals

Clear theoretical basis for work e.g. hypothesis, theoretical approach/es. Shows impact of theory on project steps

Discussion of  lab and/or field set-up and potential limitations

Explanation of data, variables and parameters and type of results to be investigated. Makes projection of outcome and shows relevance of outcome

Transforms sub-goals into a schedule of work using a Gantt Chart showing: the three key review points, milestones and deliverables.

Benefits of Agricultural Robots in Farming

The agricultural robots or “Agbots” are implemented for agriculture. As any farm grows in size, they with the volume of applications used over them have been needed for ways of automating them.

This was done manually before. Now, the tasks are performed by those autonomous machines, as they need multiple repetitions over a large area and an extended period. The usage of agricultural robots is designed as standard types of equipment for farms. This includes pesticide sprayers, various combines and tractors.

The following study has conducted a literature review and analyzed various research questions from “autonomous Agricultural Robot towards robust autonomy” from the mechanical domain. Then the theoretical methodology and experimental set-up are demonstrated. Lastly, the results and its relevance are discussed.

It is seen that in most of the cases, robots are ineffective at doing farming jobs. This has commonly needed vast amounts of materials like fertilizers and seeds or is retrieved from the harvesting field. This has been dealing with mapping and controlling precision and field for spraying pesticides. It takes place because of low weight in comparison to a tractor that makes minor soil compaction. Here, the degree of soil compaction is vital to consider mapping and monitoring that which is often done numerous times in a year. Oberti et al. (2016) explains that this is because soil compaction has been causing various issues like a decrease in denitrification and crop growth. Agrobots have been altering the scenario of agriculture beyond identification. This is from robot-assisted milking to several types of cow-herding drones. The food industry gets revolutionised through automation and robotics. The mechanised agriculture has to move as per strength. These are actual issues in modern agriculture. Conventional methods of farming are to keep up the impacts needed by the current market. The farmers in the first-world countries have been suffering from the lack of workforce. Here, the rise of automated farming has been an attempt to resolve the issues through advanced and robotic sensing. As per the current report, shown by Lopes et al. (2016) the marketplace for agricultural drones and robots are intended to reach about 30 billion dollars for the upcoming five years. Further, there are various issues in modern agriculture.   

Conventional methods have been struggling with keeping up the activities needed by the business. For example Ball et al. (2016) discussed that there is a rise in demand for nursery automation. Organizations such as “HETO Harvest Automation and Agrotechnics” has been delivering solutions regarding warehousing, potting and seeding living plants are greenhouses. The autonomous precession seeding has been assimilated robotics with geomapping. The map is generated by Bogue (2016) in his article showing soil properties like density and quality at all kinds of points at the field. Different drone companies have been offering farmers with various combined packages including robotic hardware and software of analysis. Farmers have been moving drones to the pastures initiating software through smartphone or tablet and see that collected crop information in real time. Again various ground-based robots have been supplying more detailed controlling as they get closer to crops. Few of them have been using activities like fertilising and weeding.

Challenges of using Agricultural Robots in Farming

According to Wable, Khapre and Mulajkar (2016), robots have the benefits as they access sectors where the machines have not been able to do. Here, for instance, growers of corns have been facing challenges that the plants have been growing fast to fertilise them dependably. The robots have aimed to resolve issues efficiently driving the rows of many corns. This has also been included nitrogen fertiliser directly as the ground of every plant. The ides of micro-spraying as discussed by Bloch,  Bechar and Degani (2017) has decreased the quantity of herbicide utilised in growing crops. The micro-spraying robots have used the technology of computer vision for detecting weeds and spraying targeted drops of herbicides. AG BOT II has been a solar-powered machine using that kind of processes. Mueller-Sim et al. (2017) mentioned about a LettuceBot thinning robot that has achieved the award for outstanding innovation for agriculture. It helps in deciding at what time the plants are to be kept and what to eradicate. There has been a rise in trend for followers that lead the autonomy. Here, the tractors have been following a human-driven combination of harvesters autonomously for collecting grains. For instance, EU-funded smart robots for crop projects have been progressing on various harvesting applications involving apple harvesting, sweet pepper-picking and grape picking.

Current projects:

The EU-funded "Clever Robots for Crops" project is making progress on few harvesting applications, including apple harvesting, grape picking and sweet pepper picking as shown by Jasi?ski et al. (2018). Though maximum of the agricultural robots has been applied in growing of crops, there has been emerging applications under cattle and sheep farming. This is done through assimilating capabilities of an aerial survey of various small autonomous UAV or Unmanned Aerial Vehicle multi-copter with different agricultural unmanned ground vehicles. Here the system has been surveying the field from the air and performing a targeted intervention at the ground. They have been delivering in-depth data regarding decision support with minimal invasion of users. Here the system has been adapting a broad range of crops though choosing various ground level treatment packages and sensors. Here the development has needed developments in abilities of technology for secure and exact navigations under farms. This is coordinated through multi-robot mission planning enabling a full field of survey. It has also included multispectral mapping, as demonstrated by Zakaria (2017), which is three-dimensional mapping having spatial resolution and high temporal interventions of techniques and tools. It has also included tools of data analysis from weed detection and crop monitoring and design of user interface for supporting division making at agriculture.

Applications of Agricultural Robots in Modern Farming

Besides, a gap has been present as mentioned in the artcle of Al-Beeshi et al. (2015), stopping the effective transition from scientific to societal and economic effect. This is also referred to as “Technological Innovation Gap”. EU-FP7-project-wide research is done on agricultural robotics. Here an application has been “Sweet pepper harvesting robot”. This kind of robot has been technically and economically viable as demonstrated by Baxter et al. (2018). The software and hardware models proven have developed crops used as groundwork. These successful “CROPS” software module has been from ROS or “Robotic-Operating-System” expanded and maintained at SWEEPER. Further, the gripper and effectors have been retained. Here, the patent-pending module has been grasping sweet pepper instead of any necessity of proper measurement of orientation and position of fruits. At SWEEPER, the system of cropping is intended to optimise for facilitating harvesting of robots. It was concluded at CROPS that instead of any 9DOF, one 4DOF arm has been enough to decrease the expenses. As per Ulbrich et al. (2015), for developing abilities level of abilities regarding cognitive capabilities, the plant models would be deployed for proper location of sweet peppers. Here, the model-based vision has been increasing and quickening up detection of fruits. Based on various insights of CROPS, the sensors have been placed only over the gripper. Further, LightField sensor has been introduced that has been able o record colour and 3D information at the same time.

Factoids to be considered for the research:

It is seen that most of the conglomerates of US farming have been buying various foreign areas and starting a farm there through citing the overall lesser expenses. For instance, Robert (2017) investigated that China has been buying land at Africa and sending expert workers for supervising those farms. International ranchers and farmers have been transitioning to precision methods of agriculture. This has indicated the dividing the acreage into various sub-plots, in multiple cases, proper down to distinct flora and fauna has been helping to the rise the productivity and decreasing entire costs. Different unmanned aerial vehicles can be utilised for spraying, sensing observing and mapping. Autonomous or unmanned ground vehicles have been supplying more precise movements and thus helping with precision practices. It is seen from the article of Baxter et al. (2018), the report of US Bureau of Labor Statistics that 2012 median pay for the farm workers have been about 9 dollars. On the other hand the reports from US Bureau of Labor Statistics that has been about 750,000 agricultural workers in 2012 that has been down about 3% from 2011. The approximate number of crop workers has been 74% in US-born in Central America and Mexico where more than half has been still kept undocumented as per Fortune Magazine. Furthermore, Durmu? et al. (2015) analyzed that Cropdusters has been possessing 3rd largest fatality rate taking place among professionals at the U.S. Here, 90% of crop saying at Japan has been done through different unmanned helicopters.  Again, ResearchMoz has projected that the size of agricultural marker would get increased from about 817 million dollars to about 16 billion dollars from 2013, till the end of 2020 as shown by Wable, Khapre and Mulajkar (2016).

  • What is the status of present trends and deployments of free and agricultural patterns?
  • What is the potential of future applications for autonomous agricultural robots?
  • How are these autonomous vehicles different from those of conventional ones?
  • What are the field operations for crop establishments, plant cares and selective harvestings?

Emerging Trends in Agricultural Robotics

The various purposes include the following. Example of this includes driving in top rows for a maximum of 30 seconds. Further, the robot has been continuing with operation with the fault of sensor and actuator. Also, operating under faulty and normal activities has been highly dangerous for the environment.

The sub-goals of robots in agriculture have been immense. The robots were appearing at farms in different guises and rise in numbers. The various issues related to free farm tools have been overcoming the tools. The device would turn out to be the future, and there have been essential causes to think that it has not been replacing the human driver with computers. This has indicated rethinking of how the product can be done. The production of crops has been cheaper and better with a swarm of few machines than various large ones.

The agriculture industry has been under transition. The transition has been differing as per the country, states, and regions and practiced by farming. This has taken place from primitive o traditional and from precession to experimental. This little bit of everything has been going on at every position. However, any general trend worldwide has been towards the precision agriculture that has been supplemented though developed technologies that have included robotics (Wang et al. 2016).

Various factors have been precipitating within those changes apart from an international growth of populations and availability and cost of the labor. This has included a decrease in availability and rise in an expense of water, political and processes that are regulatory. It has also included restricted tillable acreages, cheaper, better and quicker technological automation resources and changes in climate (Duarte et al. 2016). Current ranchers and farmers have been highly technical. Different digitally controlled farm deployments have been in use regularly.

There have been numerous automatic devices present for various elements of functions related to agriculture. This has extended from grafting to planting. This has included packaging to boxing, harvesting to sorting. The farmers can use software systems and various maps of aerial surveys and information for guiding the field operations (Serrano et al. 2017). Moreover, they have been using auto-steer systems including different new tractors following GPS and guidance of software. Besides, many farmers have been transitioning few operations to total autonomy. In this way, forward-thinking owners for farms of the current age has been able o skip the over slow with various developed improvements and then directly jump to autonomous and robotic automation (Jasi?ski et al. 2018).

Current Projects in Agricultural Robotics

Fault analysis is to be performed with severity analysis of every wheel, proximity sensor and inclinometer faults. Next, a non-linear model is to be implemented and designed from FDI method or Fault Detection and Isolation method (Grimstad and From 2017). Next, the critical errors are to be verified. For complementing that linear method two more new ways for FDI have been examined, that has been resulting in various objectives. The first one is to implement and design non-linear FDI method. Further, the practical goals have included extra software and hardware (Radkowski 2018). Next, an implementation and designing of proximity sensors are to be implemented on that API. After this, a space inclinometer has been performed and designed for providing rolling and pitching measurements. Lastly, implementing and developing of relays are included for disconnecting distinct wheels.

The various restrictions are listed hereafter.

  • There must not be any interference with or disability of emergency stop buttons that is mounted over to the autonomous robots.
  • They must be no providing to wheels even though the OBC gets shut down.
  • They must be able to turn the power of wheels as it gets on or off.
  • The wheels might turn off, through using various voltages supplied by the parallel port over that OBC.

As the robot operates nominally and driving to that field, the planting with sensitive crops or with crops has been containing enough width taking place between the rows. This has been fitting the wheels of that robot driving on the topmost area of the plants. However, as any error take place, the robot can get deviated from the ordinary course. This, for example, includes driving of top rows for the utmost of 30 seconds. Further, the autonomous robots have been conducting operations under a sensor or actuator fault. Here, the robots have been kept operational under erroneous situations as long as it has been possible. Various processes within faulty and normal operations have not been effectively harmful to the scenario.

Task Name

Duration

Start

Finish

Fault Detection and isolation

97 days

Mon 5/21/18

Tue 10/2/18

   Fault Analysis

92 days

Mon 5/21/18

Fri 9/28/18

   Isolability Analysis

2 days

Mon 10/1/18

Tue 10/2/18

Accept Test of Linear FDI

192 days

Wed 10/3/18

Fri 7/5/19

Accept Test of particle filter-FDI method

110 days

Mon 7/8/19

Fri 12/6/19

Accept Test of Active Fault Isolation Supervisor

2 days

Mon 12/9/19

Tue 12/10/19

   Test of steering fault isolation

1 day

Mon 12/9/19

Mon 12/9/19

   Test of propulsion Fault isolation

1 day

Tue 12/10/19

Tue 12/10/19

Hardware Test

5 days

Wed 12/11/19

Tue 12/17/19

   Inclinometer Test

3 days

Wed 12/11/19

Fri 12/13/19

   Proximity sensor test

2 days

Mon 12/16/19

Tue 12/17/19

Implementing Software

2 days

Wed 12/18/19

Thu 12/19/19

   Simulink blocks

1 day

Wed 12/18/19

Wed 12/18/19

   Stabdalone Programs

1 day

Thu 12/19/19

Thu 12/19/19

FDI method test

2 days

Fri 12/20/19

Mon 12/23/19

   Test of UIO method

1 day

Fri 12/20/19

Fri 12/20/19

   Test of Beard Fault detection Filter method

1 day

Mon 12/23/19

Mon 12/23/19

Active FI Supervisor

7 days

Tue 12/24/19

Wed 1/1/20

   Active Isolation of steering faults

2 days

Tue 12/24/19

Wed 12/25/19

   Active isolation of propulsion faults

1 day

Thu 12/26/19

Thu 12/26/19

   Kickoff Meeting: Starting Projects Right

4 days

Fri 12/27/19

Wed 1/1/20

      Getting the client on-side

1 day

Fri 12/27/19

Fri 12/27/19

      Approval process – the process and personnel for signing off deliverables

1 day

Mon 12/30/19

Mon 12/30/19

      SoW Review

1 day

Tue 12/31/19

Tue 12/31/19

      Identifying RAID (Risks, Assumptions, Issues, Dependencies) and change management

1 day

Wed 1/1/20

Wed 1/1/20


Conclusion: 

The above discussion has highlighted the vision of how different elements of crop production have been turning to an automated one. Though the current manned operations have been active over huge sets, there have been sectors to reduce the treatment scaled having autonomous machines. This has resulted in high efficiencies. This process of developments has been incremental. However, the entire idea has needed a “paradigm shift”. This has taken place in the way in which mechanization for crop production has been based on the needs of plants. Moreover, this has needed a novel approach to meet them instead of altering the current applications. Again, at modern greenhouses, there has been a rise in demand for automated labours. Here, the availability of skilled workforces accepting repetitive activities under adverse climatic conditions of the greenhouse has been declining fast. Here the rise in labour costs has decreased of capacity has put extraordinary pressure over the competitiveness of the sector of European conservatory. The study shows that the current robotisation of the labour has entered to a considerable level of readiness concerning technology.

References:

Al-Beeshi, B., Al-Mesbah, B., Al-Dosari, S. and El-Abd, M., 2015, May. iplant: The greenhouse robot. In Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on (pp. 1489-1494). IEEE.

Ball, D., Upcroft, B., Wyeth, G., Corke, P., English, A., Ross, P., Patten, T., Fitch, R., Sukkarieh, S. and Bate, A., 2016. Vision?based Obstacle Detection and Navigation for an Agricultural Robot. Journal of Field Robotics, 33(8), pp.1107-1130.

Baxter, P., Cielniak, G., Hanheide, M. and From, P., 2018, March. Safe Human-Robot Interaction in Agriculture. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 59-60). ACM.

Bergerman, M., Maeta, S.M., Zhang, J., Freitas, G.M., Hamner, B., Singh, S. and Kantor, G., 2015. Robot farmers: Autonomous orchard vehicles help tree fruit production. IEEE Robotics & Automation Magazine, 22(1), pp.54-63.

Bloch, V., Bechar, A. and Degani, A., 2017. Development of an environment characterization methodology for optimal design of an agricultural robot. Industrial Robot: An International Journal, 44(1), pp.94-103.

Bogue, R., 2016. Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 43(5), pp.450-456.

Duarte, M., dos Santos, F.N., Sousa, A. and Morais, R., 2016. Agricultural wireless sensor mapping for robot localization. In Robot 2015: Second Iberian Robotics Conference (pp. 359-370). Springer, Cham.

Dunlop, T. (2018). Agbots, next gen farming and how they can teach us about the future of work. [online] the Guardian. Available at: https://www.theguardian.com/sustainable-business/2017/may/09/agbots-next-gen-farming-and-how-they-can-teach-us-about-the-future-of-work [Accessed 23 Jun. 2018].

Durmu?, H., Güne?, E.O., K?rc?, M. and Üstünda?, B.B., 2015, July. The design of general purpose autonomous agricultural mobile-robot:“AGROBOT”. In Agro-Geoinformatics (Agro-geoinformatics), 2015 Fourth International Conference on (pp. 49-53). IEEE.

Grimstad, L. and From, P.J., 2017. Thorvald ii-a modular and re-configurable agricultural robot. IFAC-PapersOnLine, 50(1), pp.4588-4593.

Hsu, J. and Hsu, J. (2018). Rise of the Ag-Bots Will Not Sow Seeds of Unemployment. [online] Scientific American. Available at: https://www.scientificamerican.com/article/rise-of-the-ag-bots-will-not-sow-seeds-of-unemployment/ [Accessed 23 Jun. 2018].

Jasi?ski, M., M?czak, J., Szulim, P. and Radkowski, S., 2018, March. Autonomous Agricultural Robot–Testing of the Vision System for Plants/Weed Classification. In Conference on Automation (pp. 473-482). Springer, Cham.

Lopes, C.M., Graça, J., Sastre, J., Reyes, M., Guzmán, R., Braga, R., Monteiro, A. and Pinto, P.A., 2016. Vineyard yeld estimation by VINBOT robot-preliminary results with the white variety Viosinho. In Proceedings 11th Int. Terroir Congress. Jones, G. and Doran, N.(eds.), pp. 458-463. Southern Oregon University, Ashland, USA.. Jones, G.; Doran, N.(eds.).

Mueller-Sim, T., Jenkins, M., Abel, J. and Kantor, G., 2017, May. The Robotanist: a ground-based agricultural robot for high-throughput crop phenotyping. In Robotics and Automation (ICRA), 2017 IEEE International Conference on(pp. 3634-3639). IEEE.

Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., Tona, E., Ho?evar, M., Baur, J., Pfaff, J., Schütz, C. and Ulbrich, H., 2016. Selective spraying of grapevines for disease control using a modular agricultural robot. Biosystems Engineering, 146, pp.203-215.

Popular Mechanics. (2018). 5 Agro-Bots That Will Change How We Grow Everything. [online] Available at: https://www.popularmechanics.com/technology/robots/g1867/5-farm-robots-ag-industry/ [Accessed 23 Jun. 2018].

Radkowski, S., 2018. Autonomous Agricultural Robot–Testing of the Vision System for Plants/Weed Classification. Automation 2018: Advances in Automation, Robotics and Measurement Techniques, 743, p.473.

Robert, C., 2017, October. First Insights into Testing Autonomous Robot in Virtual Worlds. In 2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 112-115). IEEE.

Serrano, D., Astolfi, P., Bardaro, G., Gabrielli, A., Bascetta, L. and Matteucci, M., 2017, November. GRAPE: Ground Robot for vineyArd Monitoring and ProtEction. In ROBOT 2017: Third Iberian Robotics Conference (Vol. 1, p. 249). Springer.

Ulbrich, H., Baur, J., Pfaff, J. and Schuetz, C., 2015. Design and realization of a redundant modular multipurpose agricultural robot. In Proceedings of the XVII International Symposium on Dynamic Problems of Mechanics (DINAME), Natal, Brazil.

Wable, A.A., Khapre, G.P. and Mulajkar, R.M., 2016. Intelligent Farming Robot for Plant Health Detection using Image Processing and Sensing Device. International Journal of Engineering Science, 8320.

Wang, Z., Gong, L., Chen, Q., Li, Y., Liu, C. and Huang, Y., 2016, August. Rapid Developing the Simulation and Control Systems for a Multifunctional Autonomous Agricultural Robot with ROS. In International Conference on Intelligent Robotics and Applications (pp. 26-39). Springer, Cham.

Zakaria, R.N.B., 2017. Design of UV-Bio configuration of the NMBU agricultural robot (Master's thesis, Norwegian University of Life Sciences, Ås).

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