Performing Department
(N/A)
Non Technical Summary
The rhizosphere is presently accessible with laborious and destructive manual labor ("shovelomics"). This results in infrequent measurements by visual inspection, most of the possible data to collect is not. At the same time, our population is increasing and crop diversity is decreasing. Including logistical issues from pandemics and other potential disasters, it is clear that the sensitivity of our food supply to disruption is greater than it has ever been, with the consequences more dire. At the same time that our food supply is at risk, robotics has become an increasingly viable option for use in field. There are many companies that have arisen to address crop monitoring via drones and wheeled patrols. These same advances, however, have not translated under soil. The field of Soft Robotics, however, has produced solutions that can be translated to biomimetic approaches such as worms which, of course, have no problems maneuvering in soil. We will augment our worm-like soft robots with front positioned augers to provide a combination dig and undulate approach to soil swimming. We will incorporate humidity sensors and optical fibers into the robot to use fluorescence for sensing and imaging the rhizosphere in a continuous fashion, rather than extreme intermittency from shovelomics. The data should allow plant biologists to understand the effects of water stress on root and plant health, and provide insight to agronomists for making decisions to assist crops during varying soil conditions. Ultimately, we aim to manage our crops to produce greater yields under "worse" environmental conditions.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
15%
Developmental
15%
Goals / Objectives
AIM I. Design and develop soil swimming robots for soil sensing of the maize plant root and its rhizosphere with an above-ground carrier mobile robot to support large-scale field campaigns.AIM II. Develop the use of the soil robot collective to identify interactions between maize roots and soil water relations at critical plant development time points.AIM III. The project will share the impact and scientific findings of this project with the greater scientific community and public by implementing a coordinated set of activities that engage students, scientists, growers, and the public.i. Engagement of stakeholders at scientific meetings associated with Cornell and professional societies. ii. Postdoctoral associates, undergraduates, and graduate students will participate in interdisciplinary research that spans across the fields of robotics, remote sensing, plant physiology, and plant genetics.iii. Recruit and engage under-represented groups in research project activities.iv. Educate children and the public in robotics and plant biology through outreach activities at Cornell.
Project Methods
We will develop soft actuators with integrated proprioceptive sensing, with special attention to stretchable optical waveguides developed by Shepherd to estimate curvature/shape and as wells as force. Moreover, we want to use sensor data to develop a controller by combining a kinematic and stiffness space model and integrating it into a PID controller to control the motion of the robot in soil. To calculate the end effector position, we will first develop a model of a single module and scale-up the model to the whole robotic design as piecewise constant curvature arm (math in proposal).By correlating the kinematic actuator space and stiffness space we will measure angle and position error. These errors will directly feed into our PID control with a correction factor. The stiffness space of the actuator will be modeled through the beam theory of a cantilever beam with defined second-order nonlinear differential equations and its boundary condition.We will evaluate a replicated panel of ~200 maize hybrid varieties that are embedded within the "Genomes to Fields" (G2F) Initiative.The rich collection of maize diversity will serve as the basis of a field testbed for assessing the importance of the collected root and rhizosphere information for predicting phenotypes of agronomic and economic importance to maize breeders and producers.Optical fibers within the SoilBot will carry excitation and emitted light to and from the soil surface where a dedicated light source and spectrometer will be housed in the SoilBot platform. Line scans of fluorescence spectra will be captured both axially and transversely along the wall of the borehole, following the motion of the SoilBot head. We will feed light to a red-blue-green photodetector in the same fashion to collect reflected light for the localization of roots and identification of soil texture.In years 1-3, we will work with the technologies from Aim 1 to establish methods for rhizosphere phenotyping in maize. Year 1 will focus on the calibration of the SoilBot and sensing technology under controlled conditions with field soil. We will use a reduced subset of the target maize varieties (see Aim 3) to determine root phenotypes in controlled environment transparent acetate rhizotrons and confirm and refine root fluorescence within the targeted 365 nm UV excitation range using bandpass filtering (57) (Fig. 5). Weekly rhizotron (year 1) and field SoilBot (partial field scan year 1; complete field scan years 2-3) root scans will provide data on root growth and root system architecture (RSA) over time. In the field, our target traits will include individual root growth rate, longevity, shifts in soil moisture of the rhizosphere and global architecture (depth and breadth) of the root zone. We will work across plant developmental stages with a focus on growth stages V3 to R3 for which soil temperature and water stress are critical.