ASHS 2024 Conference

Sponsoring Professional Interest Groups
Technology: Coordinator Milt McGiffen - milt.mcgiffen@ucr.edu
Teaching Methods: Coordinator, Kathryn Orvis – orvis@purdue.edu
Controlled Environment: Coordinator, Kent Kobayashi - kentko@hawaii.edu

Supporting Professional Interest Groups
Federal Partners: Matthew Mattia - Matthew.Mattia@usda.gov
Plant Biotech: Kedong Da - kda@ncsu.edu
Ornamentals/Landscape and Turf; Youping Sun - youping.sun@usu.edu
Local Food Systems: Charles H. Parrish II - chip.parrish@pm.me

Artificial intelligence and related topics, e.g., robotics, have been a long time coming in
agriculture. For decades there have been predictions of intelligent robots replacing
humans, and large farms run by a few humans with many autonomous tractors and
other devices. But with the now widespread use of artificial intelligence in everyday life,
the moment has arrived. We developed this colloquium by casting a wide net out to all
the Professional Interest Group Chairs, and have assembled talks and demonstrations
from general topics to specific applications.

Two online meetings were held, where Professional Interest Groups officers and those interested suggested
speakers and discussed topics. Further discussions over email helped fill in the details
to create this colloquium.

We will have a block of speakers for the diverse topics we present below, as well
as panel discussions on how AI is and can be incorporated into various aspects of
Horticulture, so that there is ample time for questions and discussion.

Title: Overview of the Colloquium

Speaker: Milt McGiffen, Cooperative Extension Specialist, Department of Botany and Plant Sciences,
University of California, Riverside, CA.

AI in Ornamentals

Title: FloraScore: An App for Rapid Assessment of Pollinator Attractiveness to Annuals
and Perennial Plants.

Description: Customers are interested in buying annuals and perennials that support
pollinators. Protocols for rapid assessment in flower trail evaluations are not
available. We have developed a mobile app that can be used to analyze in real time the
users’ observational data and quantitatively rank the relative utility of observed cultivars
to pollinator communities. This app takes into account pollinator groups, relevant floral
characteristics and landscape.

Presenter: Harland Patch
Assistant Research Professor
Department of Entomology
Penn State University
549 Ag Sciences & Industries Building
University Park, PA 16802

Title: Approach to Biodiversity Protection: Employing AI and IoT Systems for the
Containment of Box Tree Moth Proliferation.

Description: The box tree moth (BTM, Cydalima perspectalis) is an invasive pest first
confirmed in Niagara County, New York in 2021. This invasive pest can significantly
damage and potentially kill boxwood (Buxus species) plants if left unchecked. This
presentation describes our advances in combining deep learning algorithms for
enhanced computer vision with IoT-enabled smart traps, to facilitate the early detection
and continuous monitoring of BTM populations and to protect the prevalent ornamental
boxwood in U.S. landscapes.

Presenter: Yanqiu Yang (she/her)
Ph.D. Graduate Research Assistant
Department of Agricultural and Biological Engineering
Pennsylvania State University
3 Agricultural Engineering Building
University Park, PA 16802

Title: Landscapes from Words: The Future of Landscape Design with AI.

Description: The ongoing text-to-graphic artificial intelligence (AI) revolution has the
potential to change the field of Landscape Architecture dramatically. The ability to
produce original high-quality graphics, manipulate the viewer's perspective of images,
and amend the rendering style through text inputs are significant advancements that will
inform new design process models. These changes can lead to expanded design
exploration, improved accessibility for non-designers to contribute to creating visual
concepts, enhanced ability to integrate data analysis and visualizations, and
streamlined collaboration between clients and project stakeholders using a shared
visual language. This talk focuses on two dimensions of change that may result from the
rapid evolution of text-to-graphic AI, including (1) faster iterations and exploration of
design options and (2) the advancement of methods that result in more inclusive and
responsive design. In the classroom, students are just beginning to acknowledge the
existence of text-to-graphic AI, which allows them to experiment with text-based design
options that allow them to quickly visualize and explore a wide range of site program
alternatives. Nevertheless, how do we manage the ethical and creative boundaries
within an academic setting? In a research context, methods supporting rapid
manipulation of both generated images and existing landscape photography represent
advances that allow for greater collaboration surrounding landscape design decisions
(Incorporating resilience strategies, protecting vernacular landscape elements that
support a sense of place, or representing new design proposals that modify the
landscape). These approaches allow stakeholders to gain remarkable advances in

influencing the design process through shared visualization development. However, as
with any emerging technology, practitioners, educators, and researchers need to
respond to the challenges presented by text-to-graphic AI by developing and testing
new design process models and public engagement techniques that can improve
landscape decision-making and streamline collaboration.

Presenter: Aaron Thompson
Assistant Professor
Department of Horticulture and Landscape Architecture
Purdue University
625 Ag Mall Drive
West Lafayette, IN 47906

Title: Developing Guidelines for Extension’s Use of ChatGPT and Other Generative AI
Tools.

AI in Extension
Dr. Masiuk is a member of ASHS and has agreed to speak.
Description: A new technological era marked by the advent of Artificial Intelligence
(AI), particularly generative AI and Large Language Models (LLMs) like ChatGPT has
necessitated the need to navigate this domain with a compass of ethicality, safety, and
effectiveness. Penn State’s experience developing guidelines for Extension’s use of
generative AI tools which will be shared and discussed.

Presenter: Michael Masiuk
Assistant Director – Horticulture Programs
Penn State Extension
342 Agricultural Administration Building
University Park, PA 16802

Panel: 30 minute panel with the above speakers, to allow time for Q&A and discussion.

Introduction and Overview

Speaker: Kathryn Orvis
Professor
Department of Horticulture and Landscape Architecture
Purdue University
625 Ag Mall Drive
West Lafayette, IN 47907-2010

Title: Digital Agriculture and AI on Specialty Crops Production

Description: Digital agriculture is the 4th agricultural revolution and Artificial Intelligence (AI) is part of it. Currently, in the "connected agriculture"; era, many technologies have been released on the marked regarding the use of multispectral
sensors for many purposes in agriculture. This talk is going to cover information on how to use Digital Agriculture online platforms to process multispectral imagery, and how AI can be used to collect individual in-field plant data.

Speaker: Luan Pereira de Oliveira
Assistant Professor and Precision Agriculture Extension Specialist
Department of Horticulture
University of Georgia
139 Engineering Building
2329 Rainwater Road
Tifton, GA 31793

Title: Bringing the Future of AI to the Farm.
Description: In this talk, we will cover the multitude of use cases where AI can be applied in farming – from weed detection and robotics to Generative AI-based farm assistants and Virtual Reality. We go through the industry trends of applied Artificial Intelligence and think big about farm automation for the future.

Speaker: Justin Hoffman
Chief Technology Officer of AgTechLogic


Title: From Concept to Impact: The Evolution of Moss Robotics through Industry-
University Collaboration

Description: Moss Robotics' journey began with a project focused on autonomous driving technology for tree nurseries, born out of a collaboration between Carnegie Mellon University, Robotics Institute and Hale; Hine Nursery in Tennessee. In this talk, we share the story of how we discovered the real value our solution could offer to growers, and how we refined our ideas through continuous iteration. This process transformed moss robotics from a simple concept into the company it is today. We will cover the steps of our evolution, emphasizing the practical benefits of combining academic research with industry needs to innovate effectively. Additionally, we look ahead to how emerging technologies might further influence our growth and the agricultural industry as a whole, aiming for advancements in farming practices that are both technologically sophisticated and grounded in real-world applications.

Speaker: Di Hu
Founder and CEO
Moss Robotics

Title: AI-Enhanced Computer Vision for Crop Monitoring in Controlled Environment
Agriculture

Description: Controlled environment agriculture (CEA) production remains expensive due to high operation costs. Growers can reduce production costs by nurturing crops with data, however, the data is highly diverse, and growers lack the expertise to analyze this data to derive actionable insights for informed decision-making. In addition, traditional crop monitoring is carried out manually, which makes it unfeasible to collect data daily to get actionable insights for high yields. Recent advancements in sensing and computing technologies, such as AI, computer vision, edge computing, and edge-
cloud integration, have opened opportunities to develop data-driven technologies to enhance decision-making capabilities. Integrating AI and computer vision technologies has emerged as a transformative toolset that can collect real-time plant data at high spatial and temporal resolutions, pivotal in optimizing resource management and maximizing production. The CE Engineering lab delves into cutting-edge computer vision applications within CEA, focusing on various applications, including phenotyping leafy greens, yield estimation, disease monitoring, and plant spacing optimization. This presentation will explore the details of lettuce phenotyping, disease classification, strawberry fruit classification, and yield estimation. We will delve into the technical aspects of these algorithms, including image processing techniques, machine learning models, and data integration strategies. This presentation will showcase state-of-the-art deep learning approaches, including segmentation algorithms, model training, and deep classifiers. Overall, this presentation aims to provide insights into the transformative potential of computer vision in CEA, offering a glimpse into the future of data-driven and sustainable CE production.

Speaker: Azlan Zahid
Assistant Professor,
Department of Biological and Agricultural Engineering
Texas A&M AgriLife Research
Texas A&M University System
Dallas, TX 75252, USA


Panel: 30-minute panel with the above speakers, to allow time for Q&A and discussion.