Statewise quinquennial average of area and production of wheat.
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Teams participating in a competition must identify best practice solutions covering a wide range of functionalities and integrate them into practical systems. These systems have to work in realistic settings, outside of the usual laboratory conditions. The competition experience helps to transfer the applied methods and tools to successful and high‐impact real‐world applications. By participating in robot competitions, young students are attracted to science and engineering disciplines. Through competition events, the relevance of robotics research is demonstrated to citizens.
\nHowever, some limitations have emerged in the past as well‐established robot competitions matured:
\nthe effort required to enter the competition grows and may present a barrier for the participation of new teams;
a gap between benchmarking complete systems in competitions and benchmarking subsystems in research may develop and limit the usefulness of the competition results to industry.
The goal of “Robot Competitions Kick Innovation in Cognitive Systems and Robotics” (RoCKIn) has been to speed up the progress toward smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europe’s societal and industrial needs:
\ndomestic service robots (RoCKIn@Home) and
innovative robot applications in industry (RoCKIn@Work).
Both challenges have been inspired by activities and their corresponding leagues in the RoboCup community [6–8], but RoCKIn extended them by introducing new and prevailing research topics, such as interaction with humans, and networking mobile robots with sensors and actuators spread over the environment (remotely controlled lamps, IP camera, motorized blinds home automation devices in RoCKIn@Home; drilling machine, conveyor belt factory‐mockup devices in RoCKIn@Work), in addition to specifying specific scoring and benchmark criteria and methods to assess progress.
\nThe RoCKIn project addressed the competition limitations identified above by (i) specifying and designing open domain test beds for competitions targeting the two challenges and usable by researchers worldwide; (ii) developing methods for scoring and benchmarking through competitions that allow to assess both particular subsystems and the integrated system; and (iii) organizing camps whose main objective is to build up a community of new teams interested to participate in robot competitions (Figure 1).
A view of the RoCKIn 2014 venue, showing the arenas and a press visit.
All these aspects of the project and their main outcomes are summarized in this chapter. We start by referring in Section 2 the two competition events organized during the project lifetime (January 2013–December 2015), listing good practices for the organization of scientific competitions in general. Section 3 presents the three RoCKIn camps and explains how they reached their goal of building a community of teams involved in robot competitions. Dissemination of robotics and its positive societal impacts, from education to technology that helps humans, is a crucial activity often connected to robot competitions. Section 4 explains how RoCKIn handled dissemination. The major outcomes of the project are summarized in Section 5, and they include scoring methods and metrics to evaluate the performance of robot systems, benchmarks, test beds designed as a standard reference, and rulebooks following the best practices in scientific robot competitions. The chapter closes with Section 6 on the project impact and lessons learned, as assessed by the project team but also by its external advisors. This chapter provides a work plan for future research triggered by robot competitions, including novel results on benchmarking, formal languages to describe competition rules objectively, dashboards for visualization of the robot state, and robot competitions as a cradle for open innovation.
Competition events were the core of RoCKIn. The test beds were developed to serve as a reference design to be used in all competitions for a given challenge, while the camps were organized to prepare new and existing teams to achieve good performances during competitions.
\nWithin the project lifetime, two competition events took place, each of them based on the two challenges and their respective test beds:
\nRoCKIn Competition 2014, in La Cité de L’Espace, Toulouse, November 24–30, 2014: 10 teams (7 @Home, 3 @Work) and 79 participants from 6 countries.
RoCKIn Competition 2015, in the Portugal Pavilion, Lisbon, Portugal, November 17–23, 2015: 12 teams (9 @Home, 3 @Work) and 93 participants from 10 countries.
Organizing each of the competition events followed and improved established best practices for the organization of scientific competitions, which are listed here for future reference:
\nsetting up a Technical Committee (TC) per challenge, mostly composed of senior researchers experienced with the competitions and the specific challenges, responsible for enforcing the application of the rulebook competition rules;
setting up an Organizing Committee (OC) per challenge, composed of researchers familiar with all the technical requirements and implementation details of the specific challenges, responsible to prepare the infrastructure and the whole setup in ways compatible with the rulebook requirements, as well as to report on that to provide transfer of information to organizers of upcoming events;
[TC + OC] issuing the call for participation, requiring teams to submit an application consisting of a four‐pages paper (named as Team Description Paper) describing the team research approach to the challenge, as well as the hardware and software architectures of its robot system, and any evidence of performance (e.g., videos);
[TC] selecting the qualified teams, from among the applicants, based on their scientific and technical merits and past competition performance;
[TC] preparing/updating and delivering the final version of the rulebooks, scoring criteria, modules, and metrics for benchmarking about 4–5 months before the actual competition dates, after an open discussion period with past participants and the robotics community in general;
[OC] building and setting up the competition infrastructure at the venue, including a vision‐based motion capture system (MCS) for ground‐truth data collection during benchmarking experiments, listing all data to be logged by the teams during the competitions for later benchmarking processing, and preparing USB pens to store the data of the actual runs of the team’s robot system;
[OC] preparing several devices and software modules required by the competition rules (e.g., referee boxes, home automation devices and their network, factory‐mockup devices and their network, objects for perception and manipulation, visitor’s uniforms and mail packages, audio files, and lexicon), and describing their characteristics and technical specifications in a wiki page where all teams can access information, including a list of frequently asked questions and their answers, to ensure consistent replies to similar questions;
[OC] establishing a schedule for the competition days and their different components (including team set up days and repeated runs of task benchmarks and functionality benchmarks);
[OC] preparing human referees to follow the teams, handle referee boxes, record scores, and all the other required tasks;
[OC] preparing the communication materials (brochure, leaflet, roller banners, banners, t‐shirts, merchandising, and schedule) for the media and general citizens and stakeholder (from academia and industry) visitors, and materials for teams (bags, badges, and schedule);
[TC+OC] establishing the adequate number of teams awarded per competition category and preparing trophies for the competition awards;
[OC] realizing the event, including the organization of visits from schools, and the availability of communicators who explain to the audience what is happening, using a simplified version of technically correct descriptions.
Best practices such as those listed above foster scientific progress (by regular rule revisions to push the challenge forward, based on feedback from participants and end‐users of the challenge scenarios), enforce technically rich approaches by the teams (by selecting them based on a team description paper) and peer monitoring (by setting up a technical committee composed of participants and other experts in the field), and enable transferring information about the competitions set up to next events (through reports prepared by the OCs), while making sure that dissemination to the general public is highly valued.
\nThe competition scoring system was deployed so as to favor performance consistency, by taking mean values of scores over several runs, rather than picking the best run. This is more adequate for benchmarking purposes, since each run of a team is designed to have the same conditions as the other team runs, and simultaneously awards the teams that can consistently achieve good performances over several runs. A possible drawback of this approach is that the ability of robot system to adapt to unexpected situations may not be fully tested. On the other hand, teams tend to improve their performance over time as they fix problems from previous runs in new runs.
Robot competitions need participating teams. Setting up a team to participate in a competition requires technical knowledge about the challenges, but also teamwork skills and experience on working and solving problems under pressure, as well as on preparing the team participation well before the competition dates.
\nRoCKIn camps were planned to build a community of teams experienced with robot competitions, and in particular with the technical details of the RoCKIn rulebooks and test bed infrastructure (e.g., interfacing the networked devices, handling the referee boxes). Simultaneously, camps acted as 1‐week school where European experts trained European students on advanced robotic topics relevant for the RoCKIn challenges, such as object recognition and manipulation, and speech understanding.
\nThree camps were organized as follows:
\nRoCKIn Kick‐off Camp, in Eindhoven, the Netherlands, June 28 to July 1, 2013, during RoboCup2013: 12 participants. The camp consisted of several lectures by the partners, on RoCKIn challenges and activities, covering subjects such as principles for benchmarking robotics, raising awareness and disseminating robotics research, and discussion on developing robotics through scientific competitions like RoboCup. In addition to the lectures, attendees got first‐hand experience of demo challenges, tests, and hardware and software solutions during the RoboCup@Home and RoboCup@Work practical sessions.
RoCKIn Camp 2014, in Rome, Italy, January 26–30, 2014: 19 teams (11 @Home, 8 @Work), corresponding to a total of 63 students and researchers from 13 countries. This camp was designed to support the preparation of (preferably new) teams to participate in RoCKIn@Home and RoCKIn@Work competitions, and featured guest lectures by Michael Zillich, Norman Hendrich, and Matthew Walter on vision‐based pattern recognition, object and people detection, object grasping and manipulation, and Human‐Robot Interaction in natural language.
RoCKIn Field Exercise 2015, in Peccioli, Italy, at the ECHORD++ Robotics Innovation Facility, March 18–22, 2015: 42 participants divided in 9 teams (4 @Home, 5 @Work). The Field Exercise has been designed as a follow up of the previous RoCKIn Camp 2014, where most of the RoCKIn Competition 2014 best teams displayed their progresses and all participants improved their interaction with the RoCKIn scoring and benchmarking infrastructure.
The selection of camp participants allowed two kinds of applications: team and individual. Teams had to submit a technical report on their existing or proposed technical approach to the RoCKIn challenges, while individuals had to submit a personal curriculum vitae. Selected individuals were assigned to teams.
\nThough the purpose of the different camps was different—as can be understood from their summary above—they were all structured similarly, i.e., including lectures by experts in particular topics and hands‐on experiments with the robots and the test beds. Mini‐competitions and awards for the best teams were created, so as to encourage team commitment and performance during the hands‐on sessions.
\nThe 2015 Field Exercise was particularly interesting because it took place at the ECHORD++ Robotics Innovation Facility (RIF) of Peccioli, Italy, funded by the European Commission. Teams gained access to the state‐of‐the‐art ECHORD++ domestic test bed and to the RoCKIn@Work industrial test bed, and had the chance to practice and improve their performance in the task and functionality benchmarks, thus showing the portability of the industrial test bed and the ability to set up different test beds all over Europe according to the RoCKIn rules. The domestic test bed was equipped with the RoCKIn ground truth system for data gathering and allowed teams to get detailed feedback on their performance. This way, public funding is leading to a network of RIFs, including RoCKIn test beds, existing ECHORD++ RIFs and other test beds recently certified by the RockEU2 project, within the frame of the new European Robotics League (ERL), where robotics researchers can go benchmark their newly developed algorithms.
Robot competitions have a crucial role on disseminating robotics research to the academic and industrial stakeholder communities, attracting young people for science and technology careers, and showing to lay citizens the impact of robotics technology on societal developments. Thus, dissemination activities focusing on the relevance of robot competitions had an important role in RoCKIn. These activities can be organized in three major categories as follows:
\nPresence in the web and social media: a web page regularly updated; Facebook page and Twitter account also regularly updated, especially during major project events, such as the camps and the competitions; videos summarizing the RoCKIn Camp 2014, the RoCKIn Field Exercise 2015, the RoCKIn Competitions 2014 and 2015 were produced and made available online on the RoCKIn website and RoCKIn YouTube channel; videos describing the main goals of the benchmarks involved in the RoCKIn challenges were also produced and made available on the RoCKIn website.
Publications, presence in major robotics conferences and workshop organization:
one key paper about the scoring and benchmarking methods used and the project activities was published on the IEEE Robotics & Automation Magazine [9];
presence in several scientific conferences, exhibitions, and industrial fairs, such as RoboCup 2013 (Eindhoven), IEEE ICRA 2013 (Karlsruhe), IEEE/RSJ IROS 2013 (Tokyo), IEEE ICAR 2013 (Montevideo), ISR/ROBOTIK 2014 @AUTOMATICA 2014 (Munich), EuRoC Challenge Design Workshop (Munich, 2014), IEEE ICRA 2014 (Hong Kong), IEEE/RSJ IROS 2014 (Chicago), INNOROBO (Lyon), IEEE/RSJ IROS 2015 (Hamburg), and ICT 2015 (Lisbon). The latter won the award for the best booth in the TRANSFORM area.
workshops on robot competitions, co‐organized with the euRathlon and the EuRoC projects, during the European Robotics Forums (ERFs) in Lyon (2013), Rovereto (2014), Vienna (2015), and Ljubljana (2016).
Event co‐location:
euRobotics AISBL decided to move, for the first time, the communication center of the European Robotics Week to La Cité de L’Espace and Toulouse during RoCKIn Competition 2014;
RoCKIn Competition 2014 satellite events: Les Journées Nationales de la Robotique Interactive—organized by LAAS/CNRS (French academic conference); Friendliness made in Midi‐Pyrénées (academia/industry networking event); Robotics EU Regions: Tell Me Who You Are (workshop on EU Robotics clusters and regions); Meetings of euRobotics Technology Topic Groups;
RoCKIn Competition 2015 satellite events: ROBOT2015—2nd Iberian Robotics Conference; EU Robotics Clusters Workshop (for Portuguese companies)—leading later to the setup of the Lisboa Robotics Cluster.
As part of the technical dissemination outputs of the project, two test beds were designed and built according to the rulebook open‐source specifications, being available for research visits by worldwide groups interested to benchmark their approaches:
\nRoCKIn@Home test bed at the Institute for Systems and Robotics of Instituto Superior Técnico, U. Lisboa, Portugal.
RoCKIn@Work test bed at Bonn‐Rhein‐Sieg University of Applied Sciences, Sankt Augustin, Germany.
Test bed details are provided in Chapters 2 and 3 of this book.
An estimated number of approximately 100 participants took part in the different activities (Camps, Competitions) organized within RoCKIn’s frame. Many of them were new to robot competitions. Thus, one of the RoCKIn’s top contributions was to build a larger community of robotics researchers interested in competitions in Europe.
\nNovel scientific and technological results are among RoCKIn’s major outcomes:
\nScoring methods and metrics to evaluate and compare performance of different robot systems designed to solve given challenges, both at the task and functionality levels.
Benchmarking methods and metrics to study the impact of functionality performance on task performance.
Open source design specifications for the test beds and rulebooks of each challenge, which take into consideration the scoring and benchmarking requirements, together with problems whose solution requires pushing the state of the art in robotics research.
Before we highlight the main contributions under the above three topics, a brief description of RoCKIn’s benchmarking and scoring systems is in order (detailed later in Chapter 4 of this book).
\nRoCKIn’s approach to benchmarking experiments is based on the definition of two separate, but interconnected, types of benchmarks:
\nFunctionality Benchmarks, which evaluate the performance of hardware/software modules dedicated to single, specific functionalities in the context of experiments focused on such functionalities.
Task Benchmarks, which assess the performance of integrated robot systems facing complex tasks that usually require the interaction of different functionalities.
Of the two types, Functionality Benchmarks are certainly the closest to a scientific experiment. This is due to their much more controlled setting and execution. On the other side, these specific aspects of Functionality Benchmarks limit their capability of capturing all the important aspects of the overall robot performance in a systemic way. More specifically, emerging system‐level properties, such as the quality of integration between modules, cannot be assessed with Functional Benchmarks alone. For this reason, RoCKIn integrates them with Task Benchmarks.
\nIn particular, evaluating only the performance of integrated system is interesting for the application, but it does neither allow to evaluate the single modules that are contributing to the global performance nor to point out the aspects needed to push their development forward. On the other side, the good performance of a module does not necessarily mean that it will perform well in the integrated system. For this reason, RoCKIn benchmarking targets both aspects and enables a deeper analysis of a robot system by combining system‐level and module‐level benchmarking.
\nSystem‐level and module‐level tests do not investigate the same properties of a robot. The module‐level test has the benefit of focusing only on the specific functionality that a module is devoted to, removing interferences due to the performance of other modules, which are intrinsically connected at the system level. For instance, if the grasping performance of a mobile manipulator is tested by having it autonomously navigate to the grasping position, visually identify the item to be picked up, and finally grasp it, the effectiveness of the grasping functionality is affected by the actual position where the navigation module stopped the robot, and by the precision of the vision module in retrieving the pose and shape of the item. On the other side, if the grasping benchmark is executed by placing the robot in a predefined known position and by feeding it with precise information about the item to be picked up, the final result will be almost exclusively due to the performance of the grasping module itself. The first benchmark can be considered as a “system‐level” benchmark, because it involves more than one functionality of the robot, and thus has limited worth as a benchmark of the grasping functionality. On the contrary, the latter test can assess the performance of the grasping module with minimal interference from other modules and a high repeatability: it can be classified as “module‐level” benchmark.
\nThe scoring framework for performance evaluation of robot systems in the Task Benchmarks of the RoCKIn@Home and RoCKIn@Work competitions is the same for all Task Benchmarks, and it is based on the concept of performance classes used for the ranking of robot performance in a specific task.
\nThe performance class that a robot is assigned is determined by the number of achievements (or goals) that the robot reaches during its execution of the task. Within each class (i.e., a performance equivalence class), ranking is defined according to the number of penalties assigned to the robot. These are assigned to robots that, in the process of executing the assigned task, make one or more of the errors defined by a task‐specific list associated with the Task Benchmark.
\nPerformance classes and penalties for a Task Benchmark are task‐specific, but they are grouped for all tasks according to three sets as follows:
\nset of disqualifying behaviors, i.e., things that the robot must not do;
set of achievements (also called goals), i.e., things that the robot should do;
set of penalizing behaviors, i.e., things that the robot should not do.
One key property of this scoring system is that a robot that executes the required task completely will always be placed into a higher performance class than a robot that executes the task partially. In fact, penalties do not change the performance class assigned to a robot and only influence intra‐class ranking.
\nIt is not possible to define a single scoring framework for all Functionality Benchmarks, as for Task Benchmarks. These are specialized benchmarks, tightly focused on a single functionality, assessing how it operates and not (or not only) the final result of its operation. As a consequence, scoring mechanisms for Functionality Benchmarks cannot ignore how the functionality operates, and metrics are strictly connected to the features of the functionality. For this reason, different from what has been done for Task Benchmarks scoring methodologies and metrics are defined separately for each Functionality Benchmark of a competition.
\nIn RoCKIn, Functionality Benchmarks are defined by four elements as follows:
\nDescription: a high level, general, description of the functionality.
Input/output: the information available to the module implementing the functionality when executed, and the expected outcome.
Benchmarking data: the data needed to perform the evaluation of the performance of the functional module.
Metrics: algorithms to process benchmarking data in an objective way.
The availability of both task and functionality rankings opens the way for the quantitative analysis of the importance of single functionalities in performing complex tasks. This is an innovative aspect triggered by the RoCKIn approach to competitions.
\nTo state the importance of a functionality in performing a given task, RoCKIn borrows the concept of Shapley value from Game theory. Let us assume that a coalition of players (functionalities in the RoCKIn context) cooperates and obtains a certain overall gain from that cooperation (the Task Benchmark scoring in the RoCKIn context). Since some players may contribute more to the coalition than others or may possess different bargaining power (e.g., threatening to destroy the whole surplus), our goal is to calculate adequately how important is each functionality to the reach a given performance in a Task Benchmark.
The RoCKIn@Home test bed (see Figure 2) consists of the environment in which the competitions took place, including all the objects and artifacts in the environment, and the equipment brought into the environment for benchmarking purposes. An aspect that is comparatively new in robot competitions is that RoCKIn@Home is, to the best of our knowledge, the first open competition targeting an environment with ambient intelligence, i.e., the environment is equipped with networked electronic devices (lamps, motorized blinds, and IP cams) the robot can communicate and interact with, and which enables the robot to exert control over certain environment artifacts.
RoCKIn@Home test bed, including the trusses for the MCS cameras on the right.
The RoCKIn@Home rulebook specifies in detail:
\nThe environment structure and properties (e.g., spatial arrangement, dimensions, and walls).
Task‐relevant objects in the environment, divide in three classes:
Navigation‐relevant objects: objects that have extent in physical space and do (or may) intersect (in 3D) with the robot’s navigation space, and which must be avoided by the robots.
Manipulation‐relevant objects: objects that the robot may have manipulative interactions (e.g., touching, grasping, lifting, holding, pushing, and pulling).
Perception‐relevant objects: objects that the robot must be able to perceive (in the sense of detecting the object by classifying it into a class, e.g., a can; recognizing the object as a particular instance of that class, e.g., a 7up can; and localizing the object pose in a predetermined environment reference frame).
During the benchmark runs executed in the test bed, a human referee enforces the rules. This referee must have a way to transmit his decisions to the robot, and receive some progress information, during the run and without interacting with the robot. To achieve this in a practical way, an assistant referee is seated at a computer and communicates verbally with the main referee. The assistant referee uses the main Referee and the Scoring and Benchmarking Box (RSBB). Besides basic starting and stopping functionality, the RSBB is also designed to receive scoring input and provide fine‐grained benchmark control for functionality benchmarks that require so. In the future, it will be developed to provide also information to the public and the team about the evolution of the robot during the task.
\nThe RoCKIn@Work test bed (see Figure 3) consists of the environment in which the competitions took place (the RoCKIn’N’RoLLIn medium‐sized factory, specialized in production of small‐ to medium‐sized lots of mechanical parts and assembled mechatronic products, integrating incoming shipments of damaged or unwanted product and raw material in its production line), including all the objects and artifacts in the environment, and the equipment brought into the environment for benchmarking purposes. An aspect that is comparatively new in robot competitions is that RoCKIn@Work is, to the best of our knowledge, the first industry‐oriented robot competition targeting an environment with ambient intelligence, i.e., the environment is equipped with networked electronic devices (e.g., a drilling machine, a conveyor belt, a force‐fitting machine, and a quality control camera), the robot can communicate and interact with, and which allow the robot to exert control over certain environment artifacts like conveyor belts or machines.
RoCKIn@Work test bed, including the trusses for the MCS cameras on the right.
The RoCKIn@Work rulebook specifies in detail:
\nThe environment structure and properties (e.g., spatial arrangement, dimensions, and walls).
Typical factory objects in the environment to manipulate and to recognize.
The main idea of the RoCKIn@Work test bed software infrastructure is to have a central server‐like hub (the RoCKIn@Work Central Factory Hub (CFH), equivalent to the RoCKIn@Home RSBB) that serves all the services that are needed for executing and scoring tasks and successfully realize the competition. This hub is derived from software systems well known in industrial business (e.g., SAP). It provides the robots with information regarding the specific tasks and tracks the production process as well as stock and logistics information of the RoCKIn’N’RoLLIn factory. It is a plug‐in‐driven software system. Each plug‐in is responsible for a specific task, functionality, or other benchmarking module.
\nBoth RoCKIn test beds include benchmarking equipment. RoCKIn benchmarking is based on the processing of data collected in two ways:
\ninternal benchmarking data, collected by the robot system under test;
external benchmarking data, collected by the equipment embedded into the test bed.
External benchmarking data are generated by the RoCKIn test bed with a multitude of methods, depending on their nature. One of the types of external benchmarking data used by RoCKIn is pose data about robots and/or their constituent parts. To acquire these, RoCKIn uses a camera‐based commercial motion capture system (MCS), composed of dedicated hardware and software. Benchmarking data have the form of a time series of poses of rigid elements of the robot (such as the base or the wrist). Once generated by the MCS system, pose data are acquired and logged by a customized external software system based on robot operating system (ROS).
\nPose data are especially significant because it is used for multiple benchmarks. There are other types of external benchmarking data that RoCKIn acquires, however, these are usually collected using devices that are specific to the benchmark.
\nFinally, equipment to collect external benchmarking data includes any server which is part of the test bed and that the robot subjected to a benchmark has to access as part of the benchmark. Communication between servers and robot is performed via the test bed’s own wireless network.
RoCKIn’s impact on the upcoming years is expected to be mostly supported by the scoring and benchmarking methods, as well as the test bed specifications, developed during the project lifetime, as they progressively (fully or partially) migrate to new European (European Robotics League[10]) and worldwide (RoboCup [6]) robot competitions. The research on benchmarking robot systems is also expected to be boosted by the introduced methods, as well as by exploiting RoCKIn major outcomes: the RoCKIn rulebooks, test beds, and datasets.
\nWe have asked members of our Advisory Board on the potential impact of RoCKIn in several directions. The following are some of the important remarks, based on each advisor background, experience, and/or role in the scientific/industrial community:
\nProspects are bright for the preparation of a proposal for a European Robotics League, aimed at designing robot competitions as benchmarking experiments, where scoring methods encourage reproducibility and repeatability of experiments and provide methods to measure performance (e.g., error between actual outputs vs. ground‐truth), while keeping the excitement of addressing a challenge and of competing with other teams to achieve the best solution to a common problem. Rules should foster developing functionalities that can be used and combined in the tasks. Algorithm and code‐sharing repository of software modules per challenge should be developed. Datasets from the competitions should be made available, while teams should be encouraged to log their runs and provide their datasets.
RoCKIn@Work is very interesting to promote the evolution from a traditional industrial robotics to an emerging service robotics in manufacturing scenarios, where robots move, share the space and tasks with humans. This scenario has a huge potential in SMEs (thousands of companies all around Europe) but two aspects need to be addressed, the cost and the flexibility (e.g., regarding setup, easy programing methods, adaptability to changes at the product or at the production). The use of a perception system to detect objects including location is very interesting as a key functionality to achieve flexibility in manufacturing with robots. Perception is also a powerful tool to compensate the lack of accuracy of less specific robots and grippers.
There is a fundamental gap between academic robotics research and robotics applications. A high technology readiness level can only be achieved for the latter if complete robotic system architectures are developed and evaluated in a strongly system‐oriented manner. However, the involved efforts needed are often left out in academic research, although they are a prerequisite for transferring research results to real robotics applications. Robotic competitions can fill this gap, as they give reward to participants’ efforts for systems development through good results. RoCKIn methodologies for robot systems integration from a scientific viewpoint enable more systematic benchmarking in competitions for intelligent robots and for transferring a rather “hands‐on” way of organizing robot competitions to a more system‐oriented research methodology. This pushes academic research toward methodologies for integration from a scientific standpoint.
RoCKIn has also laid the basis for a yet not well‐addressed aspect of future intelligent personal robots in industrial and home environments: standardization and certification. Meeting such standards with their robots, European robot manufacturers will be enabled to much better promote their high‐tech robot developments in competition with other vendors on the international market.
The European Robotics League (ERL), whose foundations were laid out during discussions that took place during RoCKIn, started in early 2016 and aims to become a sustainable distributed format (i.e., not a single big event) which is similar to the format of the European Football Champions League, where the role of national leagues is played by existing local test beds (e.g., the RoCKIn test beds, but also the ECHORD++ RIFs), used as meeting points for “matches” where one or more teams visit the home team for a small tournament. This format exploits also arenas temporarily available during major competitions in Europe allowing the realization of larger events with more teams. According to this new format, teams could get “performance points” in a given challenge for each tournament they participate to, and they get ranked based on points accumulated over the year. Teams are encouraged to arrive 1–2 weeks before the actual competition/event so to participate in integration weeks where the hosting institution provides technical support on using the local infrastructure (referee boxes, data acquisition, and logging facility, etc.). Local tournaments take place in currently available test beds. Major tournaments are part of RoboCup and other similar events. The ERL has provided a certification process to assess any new candidate test beds as RIFs for both challenges, based on the rulebook specifications and the implementation of the proper benchmarking and scoring procedures. This will enable the creation of a network of European robotics test beds having the specific purpose of benchmarking domestic robots, innovative industrial robotics applications, and factory of the future scenarios.
\nThe pool of ideas to extend and exploit RoCKIn scientific and technological results is large and exciting. We will list here the most relevant ones that came out from the 3 years of the RoCKIn experience, from the consortium members, the Experts Board members, and from Robotics researchers in general:
\nThe benchmarking infrastructure, both software and hardware, was an impressive and distinctive feature of RoCKIn with respect to other existing robot competitions and challenges. A standardized, and preferably low cost, hardware infrastructure with open source software for automated measurements and dataset dissemination, and guidelines for equipment and software set up, to be used as a reference for other competitions and research laboratories, is the way forward.
Assess robustness in performance scoring—among other examples, the ability to deal with WLAN failures (or reduced bandwidth, or big latency) should be one of the aspects that is tested, since this is essential to real autonomy and deployability (namely in home scenarios), possibly penalizing excessive use of bandwidth.
Advance toward the introduction of the semantic level, using semantic tags, i.e., all data should be accompanied by semantic meta data that described the intention of the robot actions, as well as the progress that the robot is making in this intention, at least according to what its own executor process assesses as progress, including the logging of the associated tolerances regarding the error of what the robot accomplishes with respect to the desired goal(s).
In the scoring system, trace steps toward a better balance between human’s judgment and satisfaction as the ultimate goal, and indicators that can be objectively measured, possibly including additional user‐oriented metrics like acceptability, usability, or perceived utility.
Develop (graphical) user interfaces and providing real‐time data to fill the slots of a dashboard displaying information to the attending public, e.g., information about the state of the robot actions such as grasping an object and whether the robot thinks it actually has successfully grasped it—this will force the teams to monitor and diagnose the performance of their robot systems and not only producing and storing data.
Use the RoCKIn approach as a playground for open innovation, where several teams contribute with components that need to be integrated in a “standardized” manner to build up a successful “mixed team”—domotics companies, Internet of Things research groups, care technology providers should be targeted and challenged to provide infrastructure and/or components.
Start a community effort to develop a formal language to describe robotic scenarios, robotic tasks, and robotic benchmarks, with the goal of reducing the size and increasing the objectivity of the rulebooks, so as to describe domains and tasks in a compact but nonambiguous way.
Provide the challenge rules with different levels of difficulty, so as to enable teams with different expertise levels to enter the competitions, e.g., encouraging undergraduate as well as PhD students and researchers from companies.
Enforce the usage of computer vision and computer graphics (which are emerging and trending topics in industry) in some parts of the rules, e.g., favoring visual localization and mapping.
Bring into play issues such as safety and privacy protection for robots working with aged people at home.
Project information and contacts
\nThe RoCKIn consortium is composed of the following partners:
\nInstituto Superior Técnico, project coordinator
Università di Roma “La Sapienza”
Hochschule Bonn‐Rhein‐Sieg
KUKA Roboter GmbH
Politecnico di Milano
InnoCentive
Advisory Board Members:
\nAdam Jacoff, NIST, USA
Bill Smart, Oregon State University, USA
Bruno Siciliano, University of Naples Federico II, Italy
Jon Agirre Ibarbia, Tecnalia, Spain
Manuela Veloso, Carnegie‐Mellon University, USA
Oskar von Stryk, Technical University of Darmstadt, Germany
XiaoPing Chen, University of Science and Technology of China, China
Experts board (reports on the competition events):
\nAlessandro Saffiotti, Örebro University, Sweden
Herman Bruyninckx, University of Leuven, Belgium
Tijn van der Zant, University of Groningen, The Netherlands
RoCKIn contacts:
\nWeb:
E‐mail: info@rockinrobotchallenge.eu
Facebook:
Twitter: @RoCKInChallenge
Cereals play a pivotal role to satisfy the global food demand of growing population, particularly in developing nations where cereal-based production system is the only predominant source of nutrition and calorie intake [1, 2]. The nutri-rich cereal is grown in diversified environments; globally wheat occupies around 217 million hectares holding the position of highest acreage among all crops with an annual production hovering around 731 million tonnes [3]. Wheat (Triticum aestivum L.) is one of the principal cereal crops grown worldwide and one of the important staples of nearly 2.5 billion of world population. Wheat is the major staple food crop, providing almost half of all calories in the region of North Africa and West and Central Asia. Being next to rice, wheat constitutes one of the key sources of protein in least developed countries and middle-income nations and in terms of calories and dietary intake. The crop being cultivated as winter and spring in the world, winter wheat is grown in cold countries like Europe, the USA, Australia, Russian Federation, etc., while spring wheat is grown in Asia and in some parts of the USA.
India, being blessed and enriched with a diverse agroecological condition, ensuring food and nutrition security to a majority of the Indian population through production and steady supply particularly in the recent past, is the second largest producer of wheat worldwide [4, 5, 6]. The crop has been under cultivation in about 30 million hectares (14% of global area) to produce the all-time highest output of 99.70 million tonnes of wheat (13.64% of world production) with a record average productivity of 3371 kg/ha [7]. Having a significant share in consumption of food basket with a 36% share in the total food grains produced from India and ensuring not only food security but also nutrition security, wheat is extensively procured by the government and distributed to a majority of the population; it ensures not only food security but also nutrition security. The cereal is one of the cheapest sources of energy, provides a major share of protein (20%) and calorie intake (19%) from consumption. Wheat is accessible across the country and consumed as various processed forms from prehistoric times [4].
After independence, India was net deficit in food production and had to import wheat for domestic consumption. During 1966–1967, India adopted new strategy which led the ‘Green Revolution’, especially in the production of wheat and rice. Coordinated research and several developmental and food security-based programmes in various phases have made the nation to progress closer towards ‘food and nutrition for all’ by achieving record and surplus production of wheat. After the Green Revolution, the nation has maintained strategic distance from famine even during unfavorable weather conditions. The impact of the All India Coordinated Research Project (AICRP) on wheat improvement is explicit and contributed significantly to the nation’s food security [8].
The All India Coordinated Wheat Improvement Project (AICWIP) was started in 1965 at the Indian Agricultural Research Institute (IARI), New Delhi, the nodal centre of the coordinated research. The AICWIP is one of the largest crop improvement network projects which set the dawn for the ‘Green Revolution’ in India. Under this project, several high-yielding wheat varieties have been developed which became extensively popular and adopted by the farming community. For instance, C 306, HD 2009, WL 711, UP 262, HUW 234, HD 2189, WH 147, Lok 1, HI 617 (Sujata), HD 2285, HD 2329, PBW 343, Raj 3765, PBW 502, HD 2733, HD 2967, HD 3086, DBW 17, PBW 550, GW 273, GW 322 and GW 496 in bread wheat and Raj 1555, PBW 34, HI 8498 and PDW 233 in durum wheat were developed and became the popular deliverables of the project. Apart from the aforementioned varieties, viz., NP 4, Kalyansona, Sonalika, Sharbati Sonora, WL 711, HD 1220, HD 1931 ‘SIB’, HD 2009, HD 2172, UP 262, etc., developed through the AICWIP were also cultivated beyond national borders. Several changes happened post inception of the AICWIP, and during 2017, the project has been renamed as the All India Coordinated Research Project (AICRP) on Wheat and Barley with ICAR-Indian Institute of Wheat and Barley Research as its headquarter based at Karnal (Haryana). It is a premier organization under the aegis of ICAR coordinating the multidisciplinary and multilocation testing of varieties in different AICRP centres across the different ecosystems for enhancing and sustaining the wheat production [8]. At present, there are 29 funded centres located in different agroclimatic regions across the country supporting the multidisciplinary research. The project, hitherto, has contributed in the release of around 448 high-yielding improved wheat varieties comprising bread, durum and dicoccum wheat. Over the years, prominent improvements have been made in the development arena post inception of the coordinated project (Figure 1).
Major developments in the country post inception of the AICRP.
Since the establishment of the AICRP, the productivity of wheat has increased by 2.5-folds (308%: +2.54 tonnes/ha) as furnished in Figure 2. A decadal analysis of productivity growth across major food commodities indicates that wheat production growth has outperformed rice and pulses for the past 5 decades since 1950. Overall scenario indicated that wheat production has grown at 4.72 percent per annum since 1950, the highest among other food grains [9].
Productivity trend in Indian wheat pre- and post-AICRP.
In India wheat crop is cultivated in Rabi season. It is normally sown during November and harvested between March and April. Tables 1 and 2 furnish recent scenario in wheat area, production and productivity. The cultivated area under wheat at national level has shown increasing trend, from 29.04 million hectare to 30.54 million hectare with a magnitude of 1.5 million hectare (5%) net gain in terms of area. Uttar Pradesh has largest share in area with 9.75 million hectare (32%), followed by Madhya Pradesh (18.75%), Punjab (11.48%), Rajasthan (9.74%), Haryana (8.36%) and Bihar (6.82%). However, a major expansion in wheat area was observed in the states such as Jharkhand (51%), Madhya Pradesh (27%) and Rajasthan (13%). The sharp rise in minimum support price and government’s procurement are the two important drivers which led to significant increase in the area under wheat cultivation [10].
State/UT | Area (million ha) | Change (%) | Production (million tonnes) | Change (%) | ||
---|---|---|---|---|---|---|
2008–2009 to 2012–2013 | 2013–2014 to 2017–2018 | 2008–2009 to 2012–2013 | 2013–2014 to 2017–2018 | |||
Assam | 0.05 | 0.02 | −52.35 | 0.06 | 0.03 | −43.40 |
Bihar | 2.16 | 2.08 | −3.57 | 4.63 | 4.86 | 4.98 |
Chhattisgarh | 0.10 | 0.10 | 0.59 | 0.12 | 0.14 | 13.22 |
Gujarat | 1.12 | 1.09 | −2.85 | 3.20 | 3.22 | 0.60 |
Haryana | 2.50 | 2.55 | 2.21 | 11.35 | 11.24 | −0.93 |
Himachal Pradesh | 0.36 | 0.34 | −4.26 | 0.53 | 0.66 | 24.79 |
Jammu and Kashmir | 0.29 | 0.29 | 1.83 | 0.44 | 0.48 | 10.52 |
Jharkhand | 0.12 | 0.19 | 50.99 | 0.22 | 0.38 | 70.04 |
Karnataka | 0.25 | 0.19 | −25.30 | 0.23 | 0.20 | −13.23 |
Madhya Pradesh | 4.52 | 5.73 | 26.76 | 9.45 | 16.32 | 72.72 |
Maharashtra | 1.01 | 1.05 | 4.79 | 1.61 | 1.48 | −8.27 |
Punjab | 3.52 | 3.51 | −0.40 | 16.25 | 16.61 | 2.21 |
Rajasthan | 2.63 | 2.98 | 12.99 | 8.12 | 9.31 | 14.62 |
Uttar Pradesh | 9.66 | 9.75 | 0.94 | 29.33 | 27.93 | −4.77 |
Uttarakhand | 0.38 | 0.34 | −9.89 | 0.85 | 0.81 | −4.64 |
West Bengal | 0.32 | 0.29 | −8.33 | 0.85 | 0.80 | −5.94 |
Others | 0.04 | 0.04 | −20.64 | 0.12 | 0.12 | −3.54 |
All India | 29.04 | 30.54 | 5.16 | 87.39 | 94.57 | 8.22 |
Statewise quinquennial average of area and production of wheat.
State/UT | 2008–2009 to 2012–2013 | 2013–2014 to 2017–2018 | Change (%) |
---|---|---|---|
India | 3009 | 3100 | 3.03 |
Punjab | 4617 | 4738 | 2.61 |
Haryana | 4544 | 4407 | −3.01 |
Others | 3083 | 3331 | 8.05 |
Rajasthan | 3038 | 3133 | 3.12 |
Gujarat | 2845 | 2922 | 2.69 |
Uttar Pradesh | 2724 | 2867 | 5.23 |
Madhya Pradesh | 2698 | 2843 | 5.38 |
West Bengal | 2241 | 2754 | 22.90 |
Uttarakhand | 2144 | 2375 | 10.76 |
Bihar | 2091 | 2339 | 11.87 |
Jharkhand | 1790 | 2005 | 12.01 |
Himachal Pradesh | 1602 | 1911 | 19.28 |
Jammu and Kashmir | 1511 | 1656 | 9.58 |
Maharashtra | 1466 | 1400 | −4.53 |
Assam | 1180 | 1373 | 16.39 |
Chhattisgarh | 1149 | 1328 | 15.59 |
Karnataka | 914 | 1057 | 15.64 |
Statewise quinquennial average of wheat yield (kg/ha).
The production of wheat has also showed an increasing trend, from 87.39 to 94.57 million tonnes from 2012–2013 to 2017–2018 with a magnitude of 7.18 million tonnes (8.22%). The major source of this increase in production is mainly attributed to expansion in area followed by marginal increase in productivity. Uttar Pradesh still holds the position of largest producer in the country accounting for about 28 million tonnes which is roughly 30% of the total production. Around 85 million tonnes (90%) of wheat has been produced from traditional wheat-growing regions such as Uttar Pradesh, Punjab, Haryana, Madhya Pradesh, Bihar and Rajasthan [10]. The maximum quantum jump has been noticed in Madhya Pradesh and Jharkhand which almost doubled their production from 9.45–16.32 million tonnes to 0.22–0.38 million tonnes. However, 1.4 million tonnes reduction was noticed in Uttar Pradesh during the same period which is a matter of serious concern.
The national productivity trend for wheat showed a marginal improvement, which has increased from 3009 kg/ha to 3100 kg/ha from 2012–2013 to 2017–2018 (Table 2). This rise in productivity is due to adoption of high-yielding varieties coupled with other inputs. The traditional wheat-growing states Punjab and Haryana have highest productivity than the national productivity [10]. The maximum increase in productivity has been observed in nontraditional wheat-growing states like West Bengal (23%), Himachal Pradesh (19.28%) and Assam (16.39%). However, the productivity of Haryana has declined which pose a serious matter of concern.
Quinquennial data on wheat area, production and yield for India indicates that there is a variation in crop acreage that declined to 29.58 million hectare (Figure 3). However, the production of wheat has increased significantly from 95.85 to 99.70 million tonnes. Increase in production was largely attributed to rise in productivity levels registered across the wheat-growing regions.
Quinquennial scenario in area, production and yield of wheat.
Wheat is one of the predominant staple foods and a main cereal crop of many diets around the world. Table 3 furnishes the current scenario of area, production and yield of wheat in the world. Globally wheat is cultivated in an area about 220 million hectares with a record production of 763.06 million tonnes of grain. Maximum area under wheat is in India (14%), followed by Russia (12.43%), China (11.14%) and the USA (6.90%) which altogether accounts for about 45% of global area. However, China is the major producer of wheat with a record production of 136 million tonnes, followed by India (98.51mt), Russia (85mt) and the USA (47.35mt). Around 449 million tonnes (58%) of wheat has been produced from traditional wheat-growing countries like China, India, Russia, the USA, Canada, Ukraine and Pakistan. The average yield per hectare is maximum in New Zealand (10 tonnes/ha), followed by Zambia (7 tonnes/ha) and Mexico (6 tonnes/ha). However, the average wheat yield in major wheat-growing countries is significantly low, and only China has maximum yield (5.48 tonnes/ha) followed by Ukraine, India and the USA. Despite India’s productivity being on par with the world average, the per day productivity is relatively high (20 kg/day) in comparison to other countries, viz. the USA, Uzbekistan, Hungary, Poland, Italy, Bulgaria and Romania, which predominantly cultivates winter wheat with crop cycle hovering around 275 days. However, in India, in comparison to its competing country, China, the per day productivity is almost the same. It should be noted that the winter wheat-cultivating countries do not deal with any other crop in a year, while in India, in which spring wheat cultivation occurs around 150 days duration, farmers has the choice to grow at the maximum two sole crops apart from wheat [8].
Countries | Area (million ha) | Production (million tonnes) | Yield (tonnes/ha) |
---|---|---|---|
China | 24.51 (11.14) | 134.33 (17.60) | 5.48 |
India | 29.58 (14.00) | 99.70 (12.91) | 3.37 |
Russia | 27.34 (12.43) | 84.99 (11.14) | 3.11 |
USA | 15.19 (6.90) | 47.35 (6.21) | 3.12 |
Canada | 8.98 (4.08) | 29.98 (3.93) | 3.34 |
Ukraine | 6.64 (3.02) | 26.98 (3.54) | 4.06 |
Pakistan | 8.97 (4.08) | 26.67 (3.50) | 2.97 |
Australia | 12.25 (5.57) | 21.30 (2.79) | 1.74 |
Turkey | 7.8 (3.55) | 21.00 (2.75) | 2.69 |
Kazakhstan | 11.91 (5.41) | 14.80 (1.93) | 1.24 |
World | 220 | 763.06 | 3.47 |
Area, production and yield of major wheat-producing countries (2017–2018).
Note: Figure within parenthesis indicates the percent to world.
The global wheat production has increased around 7 million tonnes (0.9%) in the year 2017–2018 in comparison to its past. The major source for the increase in production is mainly attributed to increase in productivity followed by marginal increase in area in major wheat-growing countries (Figure 4).
Annual growth in area, production and yield (2017–2018 over 2016–2017).
At the national level, there is a shift in area, production and yield under wheat during 2008–2009 to 2012–2013 vis-à-vis 2013–2014 to 2017–2018. Currently, wheat acreage is around 30 million hectares. Comparing the past two periods, the change was more prominent in wheat production, followed by area and yield (Tables 1 and 2). The average change in production was around 9%. The country on an average produced 7.3 million tonnes more than the past period. The major wheat-growing states like Punjab, Madhya Pradesh and Rajasthan have witnessed positive change in area and yield and production [6]. Surprisingly, Jharkhand registered positive change in area, yield and production, while Haryana and Uttar Pradesh, the major traditional wheat-growing states, witnessed a negative change in production due to negative change in yield. Regional disparities in area and yield had a significant impact on the wheat production. Average production in Madhya Pradesh showed an increase by 6.87 million tonnes, followed by Rajasthan (1.2 million tonnes). However, the production has declined in Uttar Pradesh (1.41 million tonnes) and Haryana (0.11 million tonnes).
Statewise comparison of area and production for 2017–2018 shows that Uttar Pradesh, Punjab, Madhya Pradesh and Haryana were the major contributors to the national production (Figure 5). However, Punjab, Haryana and Uttar Pradesh retained the status of higher productivity for many years. The scope for additional production of these states has been limited due to stagnation of wheat acreage and yield. This indicates that these states almost reached their saturation in wheat cultivation and production. Potential exists for states like Rajasthan and Madhya Pradesh to explore for additional wheat production in the coming years. Area under these states has to increase in yield at farmers’ field so as to attain higher production. The current production from these states is around 29 million tonnes which has to be doubled by 2050 with an overall production target of 140 million tonnes [11, 12, 13].
Statewise comparison of wheat area and production (2017–2018).
Production constraints are manifold and vary from crop to crop and between regions. Burgeoning population vis-à-vis increasing demand for food; growing competition for cultivable land, irrigation water and energy; intensive cropping especially in the Indo-Gangetic Plains resulting in irrational use of resources; pest-environment interaction; reduction of natural resource base; declining total factor productivity; and yield plateau (Figure 6) are the prominent challenges put forth against crop production [11, 12, 13, 14]. Wheat production not only faces the above routine challenges, but the intensity gets magnified in the context of climate change owing to its vulnerability [15, 16, 17, 18].
Production challenges in Indian setting.
In India a significant part of wheat area is under heat stress, and Gangetic plains and central and peninsular India are the most heat-stressed regions, whereas it is moderate in northwestern parts of Indo-Gangetic Plains [19]. Variability in climate is also one of the biggest environmental threats to Indian agriculture, potentially impacting the wheat production and security. In India, it has been predicted that with every rise in 1°C temperature, the wheat production will be decreased by 4–6 million tonnes. Rainfed wheat will experience a reduction in yield with 9–25% profit loss for every 2–3.5°C rise in temperature [20].
After the Green Revolution, the productivity of wheat has been significantly increased with the increase in input usage, plant protection chemicals and irrigated areas. The excessive use of fertilizer, chemicals and irrigation has degraded the fertility of the soil and also caused a reduction in groundwater table. The monocropping system led to deterioration in soil quality. If the current trend continues, the country will face a serious problem in utilization of scarce natural resources.
In India about 4.5 million hectares salt affected area is under wheat cultivation posing a major problem for canal irrigated areas [21]. Even though soil amendments and proper drainage are the more constructive solution, pace of reclamation is not substantial. This will significantly reduce the wheat yield.
As year passes, the pests of wheat have developed some resistance even though controlled under contingent situation. If not, a new range of pests and diseases have been emerging putting a serious constraint on the wheat productivity.
Adoption system and germplasm dissemination in India have been made in formal (organized) and informal (unorganized) ways [22]. Even though new improved varieties are developed and made available to farmers by NARS around, 80% of all seeds are saved by the farmers [19]. Further, a majority of farmers in India have lack of awareness of improved wheat varieties due to weak linkages [19]. The development and diffusion of improved varieties are crucial for achieving target production of wheat.
Volatility in prices of agricultural commodities has received considerable attention in the recent past among producers, consumers and policy makers. Price fluctuations create an uncertain farming situation threatening wheat production and have a negative impact on the welfare of wheat growers. Further, volatility in prices of wheat in international market hinders the smooth flow of trade across nations.
Over the years, a visible declining trend in farm holding size has been observed and is another major concern for the nation as a whole. This is caused by fragmentation of farmland owing to nuclear family system and decline in cultivable area due to urbanization. Estimate from the agricultural census (2010–2011) reports that the average operational holding in India was 1.16 ha. Among major wheat-growing states, average operational holding was highest in the case of Punjab (3.77 ha) and lowest in Bihar (0.39 ha). Declining farm size and conversion of farmland to residential area are the major setbacks with respect to food production in general and wheat production in particular.
A major concern among policy makers is the declining total factor productivity over the years owing to stagnating yield levels with increased use of inputs and resource services. It is a major concern in the intensive cropping areas wherein rice-wheat is widely under cultivation. This can be countered by adoption of improved technologies coupled with the use of optimal resources.
The constraints in wheat production are region-specific (Table 4), and it requires setting research priorities to address them. Rust, infestation of weeds such as Phalaris minor, wild oat, late sowing, low plant population, etc., were identified as the major constraints across wheat-growing zones.
Zone | Major production constraints |
---|---|
Northern hills zone | Lack of accessibility of seed of newly released variety, Phalaris minor, small land holdings, high cost of inputs, non-availability of farm machinery, yellow rust, birds, lack of knowledge among the farmers about recent technologies, imbalanced use of fertilizer, lack of irrigation facilities |
Northwestern plains zone | High cost of inputs, low price of wheat, erratic power supply, Phalaris minor, low organic matter in the soil, poor quality of seeds, non-availability of labour, untimely rain, Chenopodium album, non-availability of electricity |
Northeastern plains zone | Small land holdings, inadequacy of seeds of newly released variety, lack of information among the farmers about recently developed new technologies, late sowing, temperature fluctuations during growth, high-priced inputs, poor quality of seeds, non-availability of labour, low organic matter in the soil, non-availability of farm machinery |
Central zone | Non-availability of labour, imbalanced use of fertilizer, high temperature at maturity, limited accessibility to seed of newly released variety, temperature fluctuation during crop growth, high cost of inputs, lack of irrigation facilities, small land holding, decline in water table, untimely rain |
Peninsular zone | Low price of wheat, irregular power supply, high cost of inputs, non-availability of labour, non-availability of electricity, higher rate of custom hiring, untimely rain, lack of facilities of canal irrigation, poor accessibility to seeds of newly released variety, temperature fluctuation during crop growth |
Zone-wise production constraints in wheat.
With a limited scope for increasing the crop acreage besides the production threats and challenges at the forefront [12, 13], the production target has been fixed at 140 mt by 2050 (Figure 7) [11]. Under stable wheat acreage and given the optimistic production target, the existing average yield has to be increased from 33 to 47 Qtls/ha by 2050. Concerted research should focus to break the yield barriers in gradual manner and develop genotypes tailored for specific wheat-growing regions.
Existing production and target for 2050.
The following are the strategies set for increasing the crop productivity to achieve the set target of 140 million tonnes [9, 23, 24]:
Improvement of wheat under conventional methods
Exploitation of heterosis for developing the hybrids
Pre-breeding programme by broadening the varieties’ genetic base
Capitalizing exotic germplasm and extensive utilization
Precision phenotyping of germplasm
Mining novel alleles for genes of known function
Production of segregating populations for lines of interest identified in primary germplasm screens
Use of existing landrace x elite segregating populations to identify QTL controlling traits of interest
Production of NILs for QTL and allelic variants
Assessing agronomic performance of NILs
Development of informative genetic markers and their use in commercial wheat breeding programmes
Development of new plant types
Desired canopy structure
Rapid leaf area development
Rapid nutrient uptake
Increasing lodging resistance (robust stem)
Biotechnological interventions
Marker-assisted breeding
Wheat genome sequence and associated genomic tools
Allele mining on the basis of probing germplasm sets for specific gene sequences
Innumerable new molecular markers in genomic regions of choice to facilitate large-scale cloning of new genes
A plethora of approaches for understanding the function of each and every gene
Understanding temporal and tissue-specific gene expression in response to developmental and environmental cues
Uncovering molecular basis of complex adaptation syndromes including tolerance to various abiotic stresses
Designing of a genome-wide perfect marker system based on SNPs in entire gene space of the species
Potential of wheat transgenics and possibilities of greater public acceptance
Functional characterization of genome
Tackling disease resistance
Tackling abiotic stress-climate change
Resource management
Quality improvement
Policy reorientation [6]
Price policy
Seed policy
Credit policy
Institutional innovations like e-National Agriculture Market
Extension: transfer of technology
Economic assessment of various improved technologies for upscaling and outscaling
Promotion of resource conservation technologies [25]
Awareness among farmers of new improved varieties and production technologies for yield as well as income enhancement [26, 27]
Wheat atlas: creation and updating regional-level database on parameters like area, production, yield, yield gaps and input usage.
Analysis of benefit-cost ratio (BCR) in wheat production and development
Access to critical inputs for timely sowing like improved seeds particularly in eastern UP, Bihar, Jharkhand and Chhattisgarh; access to fertilizers, irrigation water and farm machinery [28]
Infrastructure development (roads, storage structures, market)
Agriculture transformation is of utmost importance for regional development. Cutting-edge research involving multidiscipline is the need of the hour and is expected to develop superior genotypes breaking the yield barrier. Despite being cost-intensive, development is mandatory which warrants for higher public and private investment in R&D. In addition, productivity has to be increased through massive efforts from extension personnel who serve as change agents among the farming community. A reorientation in price policy (fair price system benefiting both producers and consumers, deficient payment system to producers for difference between the market and procurement price and cash transfers to producers under colossal loss), seed policy (quality seed production and ensuring its availability for all) and credit policy (timely distribution with minimum administrative work) is highly required to support the existing production system and to carry forward. Increased access to input and output markets, revamped distributions systems, investment in rural infrastructures and skilling of the rural labour force will help immensely to increase the crop productivity. On the whole, a synergy between research-extension-policy-institutions will play an impending role to achieve the desired level of production as well as to ensure food security for future generation. The realization of the expected increase in production in agriculture will only be possible with high efficiency, high quality, resistance to biotic and abiotic stresses and by offering them to the service of the farmer by improving the stable varieties in breeding programmes.
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