F3 – Student Projects | Valley Institute for Sustainability, Technology, and Agriculture (2024)

Production-System Improvements

Life Cycle Assessment and Nitrogen Accounting of Compost Application

Organic matter amendment plays a crucial role in soil health and waste diversion. Under California Senate Bill 1383, significant quantities of organic resources are allocated to this purpose to reduce landfill waste. However, comprehensive studies assessing the impacts of compost on soil health, nitrogen cycle, hydrology, and plants are limited. This project aims to conduct a holistic assessment by measuring changes in soil carbon, nitrogen fluxes, hydrologic impacts, and plant parameters after compost application. The study will provide insights into the climate benefits of compost and its relevance to traditional agriculture, supporting the development of a circular nutrient economy and sustainable waste management.

Organic matter amendment plays a crucial role in soil health and waste diversion. Under California Senate Bill 1383, significant quantities of organic resources are allocated to this purpose to reduce landfill waste. However, comprehensive studies assessing the impacts of compost on soil health, nitrogen cycle, hydrology, and plants are limited. This project aims to conduct a holistic assessment by measuring changes in soil carbon, nitrogen fluxes, hydrologic impacts, and plant parameters after compost application. The study will provide insights into the climate benefits of compost and its relevance to traditional agriculture, supporting the development of a circular nutrient economy and sustainable waste management.

Enhancing Mechanical Harvesting Machines for Nut Trees Using a Data-Driven Model

The project aims to develop intelligent harvesting machines for nut trees that automatically adjust shaking parameters for each tree. This optimizes fruit removal, reduces tree damage, and decreases energy input and fuel consumption. The two-year project focuses on almond trees in the first year and pistachio trees in the second year. Multiple sensors will collect tree-specific data, including trunk diameter, while an AI model predicts optimal shaking parameters based on tree parameters. An acoustic- based fruit drop rate sensor will help operators determine when to stop shaking. The project outcome will be a model integrated into existing harvesting machines’ hydraulic control systems for optimal shaking frequency.

Climate Resilience and Regenerative Agriculture

This proposed dissertation aims to investigate the reasons behind the limited benefits observed in soil carbon sequestration and water conservation from regenerative agricultural practices. Focusing on California’s Central Valley, the research will analyze the impact of these practices on soil structure, hydraulic functions, and carbon mineralization. It will also explore water management strategies to optimize agricultural productivity, carbon sequestration, and water conservation. Additionally, the study will examine how long-term implementation of regenerative practices has affected soil health metrics related to water conservation and carbon stabilization. The research aims to develop effective management practices for achieving sustainable agriculture goals.

Regenerative Agriculture Innovation: Transition Towards Water Use Efficiency

California’s Central Valley produces 25% of the nation’s food using less than 1% of U.S. farmland. Regenerative agriculture is a farming innovation that focuses on soil carbon, health and water efficiency, but there is limited understanding of the process to transition to regenerative agriculture. We will conduct semi-structured interviews to understand and define regenerative agriculture. Secondly we will quantify the net water use from conventional and regenerative production across several staple crops in the San Joaquin Valley.

OptiMoistube: Optimizing California’s Irrigation Systems for Climate Resilience through an Innovative Moistube Technology

Droughts, climate change, increased heat and groundwater regulation mean that California agriculture must conserve more water while still producing food to feed the world. To compensate for the deficiency of irrigation water, this project demonstrates the increased water use efficiency of moistube irrigation compared with traditional drip irrigation. We will conduct indoor soil box experiments and test in multiple flow and environmental conditions. We will develop a numerical tool that can be applied in the design and operation of moistube for rapid adoption of the technology. Validating technologies is a good way for UC Merced to contribute to AgTech innovation and adoption.

ESFSim: Building an Experimental Smart Farm Simulation System for Interdisciplinary Research in Sustainable Agriculture

UC Merced Experimental Smart Farm (ESF) facilitates interdisciplinary research in automation, sustainability, and data processing within specialty crop farming. To overcome the limitations of physical farm research, we will create a digital simulation and analysis platform called the Experimental Smart Farm Simulation (ESFSim). This platform would leverage real-world data from the ESF to enable efficient experiments and explore ideas not feasible on a real farm. The ESFSim aims to be a small-scale, expandable, campus-shared, and cloud-based system, fostering collaboration and resource optimization. The platform’s goal is to accelerate agricultural research and contribute to industry advancements.

Reliable and Energy-Efficient LoRa Networks for Smart Irrigation and Groundwater Recharging in Orchards

Effective water resource management through smart irrigation and groundwater recharge are tools for addressing climate change and resource efficiencies in agriculture. Data applications necessitate deploying numerous sensor nodes in the field to collect sensory data for making informed water usage decisions, optimizing water use in smart irrigation. A dependable and energy-efficient data transmission system is vital for supporting these applications. We plan to develop an algorithm to determine the optimal number and location of gateways in orchards, ensuring reliable data collection with minimal gateway construction costs. Additionally, we will create a network model to establish the relationship between configurable parameters and the network’s reliability and energy efficiency. Using this relationship, we will propose an algorithm to optimize sensor node parameters, enabling smart irrigation and groundwater recharging in orchards.

Automation, Cyberphysical Systems, Robotics , and Mechatronics

Automated methods have the potential to enable sustainable agroecosystem development by reducing labor requirements. We propose planning algorithms for robotic sampling to enable resource accounting and precision agriculture operations. Our system utilizes geostatistical principles to model dynamic processes in agriculture. The algorithms prioritize sampling in areas with higher uncertainty, leveraging previous information and field knowledge. We plan field trials and system improvements, allowing the use of observations from previous surveys and multiple autonomous agents. Our work will be released under a free and open license, encouraging student engagement and experimentation in managing lands using these methods.

Digital Twin of a Smart Saltwater Greenhouse

Smart saltwater greenhouses have the potential to enhance sustainable agriculture, food security, and environmental conservation in arid regions. This project focuses on developing a digital twin of a smart saltwater greenhouse to optimize its performance and energy efficiency. The saltwater greenhouse utilizes saltwater for irrigation and can extract salt from evaporated tailwater. By incorporating AI/ML, big data, and cloud/edge computing, the digital twin enables real-time monitoring, health diagnosis, and optimal growth trajectory determination. It allows for virtual testing of configurations and management strategies before implementing them in the real greenhouse. The digital twin supports continuous monitoring and optimization to maximize yields while minimizing resource consumption and environmental impacts.