Autonomous monitoring and control of crop growth as a feedback system

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The proposed project is to model and control the growth of crops in an agricultural setting as a feedback system. The project goal is to enable growers to maximise their harvest, by taking advantage of distributed sensing to optimise the use of fertilisers and of automation for crop management. Specifically, the project will be developed through the following work packages. WP1: Dynamic modelling of lettuce growth as a feedback system. WP2: Feedback control algorithms for crop optimization. WP3: Distributed sensing technologies for crop estimation. WP4: Robotics and general automation for growth control. All work packages will be developed in collaboration with G's growers. Prototypes and testing will be also developed in the controlled setting of the Agripods available in the Observatory for Human-Machine Collaboration at the University of Cambridge. WP1 will be achieved within the first year. Choosing a suitable model for control will require an understanding of the requirements of the crops involved. Specifically, WP1 will develop open dynamic models whose inputs will be later used for control purposes. WP1 requires collecting and processing data from the grower's crops to build and test the model. WP2 goal is to develop feedback control algorithms to optimize crop growth. These algorithms must take into account availability of resources and must be robust to uncertainties. A complete study will be developed by the end of the second year. WP3 will be developed concurrently to WP1 and WP2. A first prototype for non-destructive estimation of lettuce weight is already operative. The prototype is currently undergoing calibration and general design revision. WP3 will take advantage of data and sensing technologies currently used by G's growers (Year 1). WP3 will also investigate the use of mobile robots for data gathering, with particular attention to available low-cost technologies (Year 2). Finally, WP3 and WP1 will be integrated. The goal is to take advantage of the dynamic model to improve sensing (fault detection, noise reduction). Likewise, distributed sensing will be used to refine and correct model predictions (Year 3) WP4 will investigate actuation technologies and general robotics to develop a platform for growth control (fertilizer distribution, etc.). WP4 will start from the customization of available technologies at G's growers but will also consider new technologies based on mobile robotics. WP4 depends on the completion of the other work packages. WP4 will be completed in Year 3. Major challenges for the project include: complex system dynamics in WP1; large uncertainties in WP2; limitations to the sampling rate and of the overall quality of sensor data in WP3; fault detection and difficulties in achieving high level of autonomy in WP4. A major risk for the project is the dependence on growing the crop, which is limited by seasonality and by the intrinsic slow nature of the process. Risk mitigation include creating feasibility studies in the controlled environment of the Agripods, within the Observer for Human-Machine Collaboration. This indoor controlled setting is not limited by seasonality. It allows to validate growth models and control algorithms, by simulating realistic environmental conditions. It also allows testing of prototypes for sensing and actuation.