Performing Department
School of Engineering
Non Technical Summary
Increasing population, decreasing arable land, climate change, and a declining skilled workforce pose unprecedented challenges to our ability to satisfy the growing demand for food on a global scale. Accurate assessments of multiple spatiotemporal conditions, such as evapotranspiration, are instrumental to fine tune the amount of water used in farming. Today, precise water use measurement requires the use of a pressurized chamber, an instrument that is cumbersome to operate and greatly limits the number of measurements that can be made given the need for human collection of plant specimens in the field. Consequently, critical parameters for large orchards are obtained by interpolating very sparse sample sets, thus failing to capture the inherent variability requiring precise adjustment of agricultural inputs. In this project, for the first time, we will develop a mobile robotic lab that is capable of autonomously selecting regions to sample, physically collect leaves and immediately perform on-board analysis to measure leaf water potential, improving the accuracy, precision and efficiency used in present-day pressure chamber technology. The mobile lab system features both aerial vehicles as well as ground robots, and during the project we will design a novel robotized pressure chamber enabling the measurement of leaf water potential at scale. The system will be tested and validated in the field in four different agronomic testbeds in California on four different perennial crops in collaboration with commercial partners.This project will develop the scientific and technological foundations to create a new mobile robotics lab to perform sampling and analysis of water leaf potential at spatiotemporal scale not achievable with current technologies. The PIs will work on hardware/software co-design of novel actuators to autonomously acquire and analyze specimens in the field. We will tackle fundamental questions about coordination of heterogeneous robotic systems operating under resource constraints, and perception algorithms to extract how to best perform leaf water potential measurements observing human behaviors. Finally, collected data will be used to quantitatively confirm or disprove our hypothesis that current sampling practices fail to capture the existing heterogeneity in leaf water potential.The proposed system has broad applicability andcan be used to improve agriculture practices in a wide variety of crops throughout the US with the potential of great savings in the use of water and agrichemicals. Both participating universities are Hispanic Serving Institutions (HSIs), and students from all backgrounds will be involved. Findings will be presented at leading conferences and papers will be freely made available. Hardware designs and code will be open source and data collected during the project will also be made freely available to the scientific community. Results will be disseminated to the broader public through the University of California TV, and existing outreach initiatives at both institutions will be leveraged to engage K-12 students, as well as industry stakeholders.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
(N/A)
Developmental
30%
Goals / Objectives
The overarching goal of this project is to develop and deploy heterogeneous teams of autonomous robots (specifically, aerial and ground robots) to enable frequent and dense sampling in the field. The motivating hypothesis is that an increase in sampling density and frequency can indicate noticeable spatiotemporal variability in water potential that would remain otherwise undetected because of insufficient sampling resolution. In pursue of this goal, the project tackles four key objectives, described below.Objective 1 --Robotized Pressure Chamber Development: We will develop all the components to sample individual leaves and assess their water potential, autonomously. The system will consist of a ground mobile base, a manipulator, a pneumatic control board, and an integrated leaf acquisition and pressure chamber device. Components will be optimized through co-design of hardware, sensing, and control.Objective 2 -- Visual Sensing for Accurate Determination of Leaf Water Potential: We will rely on visual sensing to determine the leaf water potential. To achieve so, we will establish new image quality enhancement algorithms using limited training data and develop new algorithms for imitation learning form human experts to help address the long-standing challenges of bubbling and presence on non-xylem water when measuring leaf water potential.Objective 3 -- Multi-robot Coordination and Planning: We will study how to effectively coordinate multiple aerial and ground vehicles to perform targeted sampling in areas of interest while being cognizant of the inherent operational constraints due to the limited energy supply provided by the batteries. Coordination tasks will be cast as optimization problems related to the orienteering problem.Objective 4 -- Evaluation: We will test our developed system in the field at separate locations in northern and southern California where at least four different specialty crops (grapes, almonds, citrus, and avocados) are grown. The validation task serves two purposes. First, it will provide feedback for the iterative evolution of the design and implementation of the hardware/software system we will develop. Second, collected data will be used to test our working hypothesis that current sampling practices fail to capture spatiotemporal variability in leaf water potential.This project iscollaborativebetween UC Merced and UC Riverside
Project Methods
Efforts: Two types of experiments will be performed during the four year project. The first set of experiments aims at perfecting the accuracy of the robotized pressure chamber we will develop. To this end, measurements obtained with the robotized pressure chamber will be cross-validated with leaf water potential measurements obtained with a manually operated portable pressure chamber. These initial experiments will ensure adequate data accuracy before we perform the data analysis process described in the next subsection. We anticipate these experiments to take place in the first and second year of the project. Once the robotized pressure chamber has been perfected, data collection experiments will involve the entire system. We note that UAVs and ground robots do not need to operate at the same time, but it is instead foreseeable that imagery collected by the UAV will be processed off-line. This is an acceptable approach because the underlying physical phenomena are slow varying. Ground robots will then collect data from both the pressure chamber and the soil probe and store them as entries with spatio-temporal references for the subsequent data analysis.Evaluation:The co-robot system we will develop will be tested in the field to prove or disprove the hypothesis that current interpolation approaches based on few measurements per tens of acres fail to capture significant variability in leaf water potential. The value of the hypothesis is that more accurate estimates are essential for tuning inputs and implementing precision agriculture practices.All outdoor robot testing will adhere to any applicable regulations, as for example in the case of aerial robot testing which is regulated by UC and FAA policies (and in which case we will work with the UC Center of Excellence on Unmanned Aircraft System Safety hosted at UC Merced). All robot testing and equipment use will adhere to Standard Operating Procedures (SOPs) developed by the PIs and approved by both institutions' Environmental and Health Safety (EHS) departments.In addition to simulation models and lab tests, the proposed system will be deployed and evaluated in various testbeds where different specialty crops are grown. Specifically, we have identified four testbeds to evaluate system performance. These testbeds include (1) an experimental vineyard managed by one of our industrial partners located in Firebaugh (Fresno county, CA), and (2) almond orchard located in Merced County. In Riverside County, in consultation with local commercial partners, tests will be performed in the Agricultural Operations (Ag Ops) facilities managed by UC Riverside where (3) citrus and (4) avocados are grown. These testbeds are geographically distributed and used to grow different crops, thus allowing to operate the proposed solution under heterogeneous conditions.