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Study of In-Plant Sensing for the Precise Control of Water Use in Agriculture

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Study of In-Plant Sensing for the Precise Control of Water Use in Agriculture STUDY OF IN-PLANT SENSING FOR THE PRECISE CONTROL OF WATER USE IN AGRICULTURE A Thesis Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Master of Science by Rui Gao December 2020 c 2020 Rui Gao ALL RIGHTS RESERVED ABSTRACT Climate change in recent years has induced extreme weather conditions that negatively impact food production and cause increased crop losses. As the world population grows, there is an emerging need to make agriculture more robust, efficient and productive. Understanding the plant dynamics becomes more important than ever for enhancing the agricultural water use efficiency (WUE), a key factor in shaping long-term agricultural development. Plant water stress is dynamic, resulting from rapid changes in evapotranspiration (ET) due to cou- pling to the atmosphere and slow changes in water availability due to soil dehydration. Stem water potential (SWP) integrates the water stress across the soil-plant-atmosphere-continuum (SPAC) and is therefore useful for scheduling plant-based precision irrigation. The micro-tensiometer (µTM) can provide valuable physiological information about a plant’s drought response by monitoring the plant’s ability to manage its water needs when facing en- vironmental stress. With its continuous and real-time measurements, the µTM opens up a new opportunity to investigate system control strategies for improving WUE. In this thesis, we study the possibility of integrating the µTM within a water stress monitoring feed- back framework for controlled water delivery to important fruit crops such as apple. We present our exploration of plants’ responses to well-controlled irrigation events. We discover that the transient of root water uptake is likely to change after the growing season, resulting in increased sensitivity to daytime (more stressed state) rewatering. Additionally, we find that the plant and the soil become more decoupled as dehydration proceeds, resulting in persistent disequilibrium. The acquired data will be used to continue refining the existing hydraulic circuit models of apple under drought stress, thus finalizing a virtual representation of this speciality crop, or “digital twin”. The combination of the µTM and the model provides a valuable tool to reveal the full dynamics behind plant water stress and better agricultural water management across different phenological stages. BIOGRAPHICAL SKETCH Rui grew up in Beijing, China. In 2014, Rui began her undergraduate education at the University of Rochester. In Rochester, Rui studied under Dr. Ching Wan Tang and Dr. Alexander Shestopalov on projects related to the organic light-emitting diode (OLED). Four years later, Rui graduated Magna Cum Laude with Highest Distinction. Continuing her exploration in the field of Chemical Engineering, Rui started her graduate study at Cornell University and conducted research under the supervision of Dr. Abraham Duncan Stroock (Chemical Engineering, Ithaca), Dr. Lailiang Cheng (Horticulture, Ithaca), and Dr. Fengqi You (Chemical Engineering, Ithaca) on the work presented in this thesis. iii ACKNOWLEDGEMENTS This work is supported by many people and institutions, and my gratitude goes to all of them. I would like to identify here those that make the most difference on my research journey and life over the last 2 years and beyond: I would like to thank my advisor, Dr. Abraham Stroock, who has always been supportive and in- spiring. His exceptional mentorship and insights have helped me develop into a better researcher. I would also like to thank my committee members, Dr. Lailiang Cheng and Dr. Fengqi You, who have provided invaluable assistance with my research. I am also grateful to the Department of Chemical and Biomolecular Engineering for the diverse and inclusive environment, and to my research colleagues, Siyu Zhu, Annika Erika Huber, Corentin Bisot, Weichen Zhou, Hanwen Lu, Piyush Jain, Siyu Bu and Sahil Anand Desai, who have inter- acted with me daily and provided help throughout my research. Lastly, I would like to thank my family and friends for their unconditional support and understand- ing throughout my life. iv TABLE OF CONTENTS Biographical Sketch . iii Acknowledgements . iv Table of Contents . .v 1 Introduction 1 1 Context and Motivation . .1 1.1 Water Resource Utilization in Agriculture . .1 1.2 Water Stress Physiology . .2 1.3 Challenges in Process Control and Optimization of Plant Water Use . .4 1.4 Opportunities and Approaches . .6 2 Background . .7 2.1 Water Potential . .7 2.1.1 Metastable Vapor-Liquid Equilibrium (MVLE) . .8 2.2 Soil-Plant-Atmosphere Continuum (SPAC) . .9 2.2.1 Water Transport in Xylem . .9 2.2.2 Soil-Root-Leaf Water Relations . 11 2.2.3 Stomatal Regulation . 11 2.2.4 Construction of Circuit Models of the SPAC . 13 2.3 Micro-Tensiometer (µTM) . 20 2.3.1 Micro-Electrical-Mechanical System (MEMS) . 21 2.3.2 Tensiometry . 22 2.3.3 Other Available Water Potential Sensors . 23 2.4 Process Control . 24 2.4.1 On-Off Control . 26 2.4.2 Proportional-Integrator-Derivative (PID) Control . 27 2.4.3 Model Predictive Control (MPC) . 28 2 Materials and Methods 30 1 Introduction . 30 2 Micro-Tensiometer Preparation . 30 2.1 Fabrication . 30 2.2 Packaging . 32 2.3 Calibration . 34 2.3.1 Temperature Calibration of Bridge Offset and PRT . 34 2.3.2 Osmotic Calibration of Bridge Response . 35 2.3.3 Uncertainty Assessment . 37 2.4 Embedding . 38 3 Field Experiments . 41 3.1 Growth Information of Apple Trees . 41 3.2 Design of Irrigation Control System . 42 3.3 Setup at the Cornell Orchard . 46 4 Comparison between Measured and Modeled ET .................. 48 5 Comparison between µTM and SPC Measurements . 50 6 Conclusion . 51 v 3 Results and Discussion 52 1 Plant Response to Controlled Irrigation Experiments . 52 1.1 Well-watered State . 55 1.2 First Dry-down Cycle . 58 1.3 Second Dry-down Cycle . 65 4 Future Work and Conclusion 71 1 Simulation: Model the Potted Apple Trees under Water Stress . 71 1.1 Stratify the Soil Compartment into Two Layers . 71 1.2 Incorporate Mechanistic Stomatal Regulation . 72 2 Experiment: Continue Exploration of Apple Stress Physiology . 73 2.1 Experiment across Different Seasonal Phenology . 73 2.2 Experiment with Different Rootstocks . 73 3 System: Improve the Irrigation System for Scalable In-field Deployment . 74 3.1 Refine the Power Supply and Integrate with Cloud Interface . 74 3.2 Determine the Density of µTM Installation . 76 4 Conclusion . 77 5 APPENDIX . 79 5.1 µTM Data Acquisition (CRBasic Editor) . 79 5.2 µTM Calibration Program (Python) . 83 5.3 Irrigation Control (Python) . 91 5.3.1 Check Internet Connection . 91 5.3.2 Communication between the Dataloggers and the Pi . 92 5.3.3 Launch Irrigation at Set Time . 102 5.3.4 Download Data from the IoT Platform . 108 Bibliography 118 vi LIST OF TABLES 1.1 Comparison between major approaches for irrigation management. .5 3.1 Constant and variable hydraulic parameters used in the 2C model (tuned based on References17, 56) for potted apple trees. 55 vii LIST OF FIGURES 1.1 Risk to water supply sustainability due to climate change effects. .2 1.2 Integration of the µTM and process control in irrigation management. .7 1.3 Schematic diagram of water flow through the SPAC down a gradient in water potential (modified from Stroock et al.31)....................... 10 1.4 Theoretical sketch of diurnal variations of the soil-plant dynamics.41 ........ 12 1.5 Cross section of a stomate.43 ............................. 13 1.6 Schematic diagram of a two-compartment (2C) model under water-stressed con- dition. 14 1.7 Empirical relationship between soil water content (Θ) and soil water potential (Ψs). 18 1.8 Empirical relationship between the resistances (Rr and Rs) and Ψs.......... 19 1.9 Working mechanism of a µTM. 21 1.10 Illustration of metastable vapor-liquid equilibrium using a water reservoir. 22 1.11 Comparison in size between a µTM and a SPC. 24 1.12 Block diagrams of three major systems. 25 1.13 Block diagram of a simplified closed-loop control system. 26 1.14 Schematic diagram of basic concept behind MPC.29 ................ 29 2.1 Schematic illustration of the micro-fabrication processes (Courtesy of Dr. Michael Santiago). 31 2.2 Diagrams of a packaged µTM............................. 33 2.3 Temperature calibration curves of a sensor used in Summer 2020. 35 2.4 Osmotic calibration curve of a sensor used in Summer 2020. 37 2.5 Schematic diagram of the cross-sectional view of the interface between a µTM and the tree xylem. 39 2.6 Embedding procedure of a µTM to an apple tree. 41 2.7 Instrumentation diagram overviewing the experimental set in Summer 2020. 43 viii 2.8 Comparison at tree-level sensing between (a) Summer 2019 and (b) Summer 2020. 45 2.9 Instrumentation involved in Summer 2020 (control center not presented). 47 2.10 Comparison of measured and modelled evapotranspiration (ET). 48 2.11 Comparison of measured and modelled cumulative ET............... 49 2.12 Comparison between SWP measured by the micro-tensiometer (µTM) and by the Scholander Pressure Chamber (SPC). 51 3.1 Plant water responses observed in Summer 2019 (Courtesy of Siyu Zhu). 53 3.2 Full dynamics of ΨµTM1 with respect to ET and Ψsoil variations over the experi- mental period. 54 3.3 Full dynamics of ΨµTM2 with respect to ET and Ψsoil variations over the experi- mental period. 54 3.4 Dynamics of plant water stress and environmental variables from Sept. 2nd to 19th. 56 3.5 Zoom-in dynamics of measured and modeled SWP during irrigation and fertiga- tion events on Sept. 10th................................ 58 3.6 Dynamics of plant and soil water stress in fluctuating environment from Sept. 15th to 26th........................................
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