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.

Growth-promoting Bacteria in Improving the Biophysical Parameters of Cherry Tomatoes (Solanum lycopersicum L.)  - Henrique Oliveira
Promoting Controlled Environment Agriculture Activities At Campus-Wide Events - Kent Kobayashi
Rapid Detection of Herbicide-Resistant Weeds Utilizing Novel Full-Spectrum Imaging and a Hyperparameter-Tuned Convolutional Neural Network (CNN) - Pauline Victoria Estrada
Puʻuhonua Kauluwehi: Maui Wildfire Rapid Response Strategies for Agroecosystem Resilience and Community Well-Being - Nicolette van der Lee
Enhancing Hot Pepper Quality and Yield through Smart Irrigation Strategies - Harmandeep Sharma
Spraying Drone Efficiency: A Comparative Study of Application Rate and Surfactant Addition - Lucas Sales

As a strategy, the use of plant growth-promoting bacteria (PGPB) in agriculture has stood out because they interact symbiotically with plants, promoting their growth directly or indirectly. The objective of this study was to evaluate the effects of the effects of inoculation with Bacillus subtilis ATCC 23858 and Burkholderia seminalis TC3.4.2R3 on the biopshycal characteristics of the plants, technological attributes of the fruits, and productivity of common cherry tomatoes . The experiment was arranged in a randomized block design with three treatments: i) inoculation with Bacillus subtilis ATCC 23858; ii) inoculation with Burkholderia seminalis TC3.4.2R3; and iii) non-inoculation, with eight replications. The data were subjected to analysis of variance (ANOVA) using the F-test followed by the Tukey test (P

How can we help students, the public, and stakeholders become familiar and engaged with controlled environment agriculture (CEA) and its benefits? Besides offering undergraduate courses such as TPSS 300 Tropical Production Systems and TPSS 491 Experimental Topics "Controlled Environment Agriculture" we sought other ways to accomplish this. The objective is to describe how we use displays about our CEA lab at campus-wide events to help inform audiences about CEA and its technology. Various events at the University of Hawaii at Manoa (UHM) enable colleges, departments, units, and individual laboratories the opportunity to showcase their programs, curricula, and research. At these campus-wide events, we set up table displays that explain CEA and highlight our CEA research. Our displays exhibit various aspects of the technology used in CEA such as LED (light-emitting diodes) lights, hydroponics, and greenhouse materials. We display high tech acrylic greenhouse coverings and walls, smart glass, photoselective shadecloths, and light spectrum control plastic films to show recent developments in greenhouse coverings. Hydroponic principles are explained through the use of micro-hydroponics, dwarf vegetables grown under LED lights, and hydroponic kits. A display using simulated Martian soils and LEGO® figures shows a Martian landscape with a plastic dome greenhouse with plastic vegetables growing inside. The audience gets to experience a hands-on working miniature grow tent, a replica of actual grow tents, to demonstrate how CEA experiments are conducted using grow tents with manually controlled red, blue, and white LED lights and fans. We have a shadecloth covered PVC pipe box with red and blue photoselective shadecloths and LED light placements on top, sides, and intracanopy to explain light spectrum and light placement. The Lunar/Martian greenhouse model displays an example of how plants could be grown on extraterrestrial bodies such as the moon and Mars. The display shows a cutaway view of a greenhouse installed below the soil surface for protection from radiation. Natural light is supplied with light pipes and artificial light is supplied with LEDs. Our lab’s table displays have been well received by people stopping by our tables. The campus events provide the opportunity for students to assist in staffing the tables and talking about CEA and their research. We also discuss CEA research opportunities provided by the UHM Undergraduate Research Opportunities Program (UROP) and the UHM Hawaii Space Grant Consortium Program.

Every year, farmers around the world lose more than $95 billion from uncontrolled weed infestation. Herbicide-resistant weeds, also known as “superweeds”, are fast becoming a significant part of this weed problem and are a significant threat to crop production and food security. Late detection of resistant weeds leads to increasing economic losses and severe environmental damage. Traditionally, genetic sequencing and herbicide dose-response studies are used to detect herbicide-resistant weeds, but these are expensive and slow processes. To address this problem, an AI-based superweed identifier program (SIP) was developed to quickly and accurately distinguish herbicide-resistant from susceptible chickweed (Stellaria media). A regular camera was converted to capture light wavelengths from 300 to 1,100 nm. These full spectrum images were used to develop a hyperparameter-tuned convolutional neural network (CNN) model utilizing a “train from scratch” approach. This novel approach exploits the subtle differences in the spectral signature of resistant and susceptible chickweed plants as they react differently to herbicide treatments. The SIP was able to identify resistant chickweed to acetolactate synthetase (ALS) inhibitor herbicides as early as 72 hours post treatment at an impressive accuracy of 85%. It has broad applicability due to its ability to distinguish resistant from susceptible chickweed plants regardless of the type of ALS herbicide or dosage rate used. Utilizing the superweed identifier program will allow farmers to make timely interventions and develop more effective and safer weed management practices. This can optimize yield, reduce herbicide use, minimize environmental harm, prevent herbicide-resistant weed proliferation, and improve overall public health.

The Puʻuhonua Kauluwehi project aims to develop a rapid response to the recent Maui wildfires by collaboratively establishing a network of biocultural refuges supporting the cultivation of native plants to accelerate landscape-scale agroecological resilience, food security and community well-being strategies. Puʻuhonua Kauluwehi is a Hawaiian phrase describing regenerative agroecosystem areas that provide shelter for native vegetation, attract native birds and insects, and serve as a source of thriving launching points to revegetate the landscape through community engagement. In Hawaiʻi, establishing biocultural refuges is even more critical as the unique ecosystems of the islands continue to come under threat from invasive species, drought, commercial development, lack of ecosystem management and are more at risk due to the dependence on imported response and aid resources from the mainland as demonstrated by the devastating impact of the Maui wildfires in August 2023. The project’s specific objectives are to: (1) Provide applied research and GIS mapping services that integrate water quality testing, soil testing and native plant and tree cataloging in one accessible database for the growing coalition of local agricultural and conservation organizations responding to the wildfires; (2) Develop strategies to ensure all children, youth, and adults have access to abundant local food during and after wildfire disasters through a network of seed orchard, seed bank, nursery and food hub partners; and (3) Design extension and non-formal community education initiatives to address the health and well-being of children, youth, and adults affected by wildfire disasters through work-based agroecosystem and stewardship training in the Kauluwehi Biocultural Garden for 300 participants. The Puʻuhonua Kauluwehi restoration project, led by University of Hawaii Maui College, will share initial outcomes of launching a technology platform to connect critical nodes of the Maui wildfire response into a thriving network that will serve as a social-ecological incubator for the positive impact of vibrant and culturally authentic landscapes and redefine the value of agroecosystems in Maui’s unique context for disaster recovery.

Hot peppers (Capsicum chinense) are attracting increasing attention due to their rich reservoirs of secondary metabolites, notably capsaicinoids, which are in high demand across various industries such as culinary, cosmetic, and pharmaceutical. Consequently, there has been a surge in the number of new pepper growers emerging throughout the United States. Despite ranking fifth in pepper production, North Carolina’s pepper cultivation remains smaller compared to other states known for hot pepper production. Additionally, the southern U.S. anticipates an increase in extreme weather events such as droughts and floods. Thus, there is a pressing need to identify the most suitable pepper cultivars and implement efficient production management practices tailored to local climatic conditions to maximize both crop production and quality. To address this need, the current study was conducted at Reid Greenhouse, North Carolina Agricultural

Current agricultural practices are facing several challenges because of the use of large and heavy machinery used in the fields. The benefits of covering large areas to meet the time of spraying crops is becoming questionable because the heavy machinery (large self-propelled boom sprayers) also can cause soil compaction and require large amounts of fuel and technical labor to be operated. Moreover, spraying drones are emerging as a pivotal technology in modern agriculture. They serve multiple purposes, from measuring and understanding fields using sensor and camera-captured images to acting as spray applicators for a wide range of products e.g.,including herbicides, pesticides, fungicides, and fertilizers. As a novel technology, spraying drones overcome some of the challenges faced by traditional methods. For instance, they can initiate applications in specific areas that require treatment, thereby avoiding issues like soil compression and unnecessary use of cultivated areas. This enhances precision while reduces losses in the field. However, defining application rate and the impact of adjuvant products is still scarce in previous studies. Therefore, in this study, we analyzed whether the coverage area is influenced by application rates and surfactant addition. The study was conducted in a carrot crop field. Water-sensitive papers were placed on the top leaf and at the bottom of the plants to quantify the coverage area. The measured area comprised a swath of 40 feet and a drone route of 100 feet. Measurements were performed in 9 crop-rows, each row with three hydrosensitive papers spaced in 33 feet apart. A multirotor spraying drone XAG P100Pro with Atomized Nozzles was used to apply spraying rates of 5 and 10 gallons per acre, both with and without surfactant addition. Results showed more coverage area on the top leaf than at the bottom of the plants. Similarly, when 10 gallons per acre were applied, it produced a higher covered area. However, there was a difference when applied 10 gallons with and without adjuvant. By applying adjuvant, the trial proved more efficient in reaching the plants. Conversely, when 5 gallons were applied, the surfactant did not contribute to either the top leaves or bottom part. Therefore, our results are promising and contribute to the enhancement of technology in agricultural production. The insights allow from farms to research centers to improve the spraying drone application, guaranteeing a more sustainable environment.

Advanced 3D Imaging for High Throughput Phenotyping of Horticultural Crops - Yu Jiang
Integrating UAV Imagery and AI to Forecast Vidalia Onion Yield and Quality - Marcelo Barbosa
Deep Learning Application for Field Phenotyping of Shoot Structure in Grapevine - Soichiro Nishiyama
Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew - Bo Liu
Effect of Innovative Laser Labeling Technology on Fresh Produce Quality and Safety - Manreet Bhullar
Cover Crop Decision Support Tools: Exploring the new suite of online cover crop tools - Esleyther Henriquez Inoa
CFD-based aerodynamic analysis under high wind velocity environment for multiple greenhouses - Anthony Kintu

Understanding plant growth and development is crucial for insights into plant structure and function, and recent advancements in AI-driven 3D imaging technologies have revolutionized the acquisition and analysis of high-fidelity plant models. These technologies enable accurate and rapid measurement of phenotypic traits, aiding breeders in developing new varieties and helping horticulturists optimize production management. The overarching goal of this study was to establish an AI-based 3D imaging and analysis pipeline specifically designed for detailed examination of horticultural crops at the organ level within controlled environments. We developed a robotic platform equipped with a rotating base and a high-resolution camera mounted on a robotic arm, allowing comprehensive imaging from any angle around the plant. Utilizing this robot, we generated 3D models of 30 hemp plants from two growth-rate categories in controlled environments, on a weekly basis. An AI model was developed to segment these 3D models into stems, branches, and leaves. Morphological traits were extracted from each category of the segmented organs, including stem length (i.e., plant height), stem diameter, branch length, branch diameter, leaf number, leaf area, and leaf aspect ratio. These measurements contributed to a classification model capable of distinguishing between fast and regular growth rates. Experimental results showed that the 3D imaging-derived measurements were highly correlated with human-derived measurements. In addition, the extracted traits were used as quantitative descriptors to classify hemp cultivars with different growth rates in CEA. Therefore, the developed pipeline can be used as an effective and efficient tool for breeding programs and CEA production management in the future.

Forecasting yield and quality of Vidalia onions allows the stakeholders to make decisions on the best time and place to harvest. While yield defines an important quantitative parameter, conversely, sweetness emerges as timely factor of quality. Traditionally, measuring these parameters requires a field team and routine laboratory for the assessments, making it a subjective, time-consuming, labor-intensive, costly, and not-scalable approach. However, image technology and artificial intelligence (AI)-based methods could improve decision-making strategies. In this study, we collected unmanned aerial vehicle (UAV) multispectral images of two Vidalia onions fields from crop establishment until the harvest date, totaling six sets of images for each field. Each flight was performed with approximately 15 days apart. At the harvest date, 50 samples were collected in each field to determine yield, while 10 samples were used for sweetness. To ensure the robustness of the dataset, both fields were combined into a single dataset. Consequently, we used machine learning (ML) algorithms to perform predictive models, namely multiple linear regression (MLR), random forest (RF), and support vector machine (SVM). The dataset was split into 70% and 30% for training and testing, respectively, and the predictions were performed using the test dataset. Regarding the assessment of the models, we used the metrics namely coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE). The models with higher R2 and lower MAE and RMSE were the bests. Notably, the considerable correlation between yield and spectral data made the MLR model perform well as more complex models such as RF. Conversely, when there was a weak correlation between the sweetness and spectral data, RF model could perform much better. In short, both models (MLR, RF, and SVM) could perform well into a predictive model, which highlights the strength of spectral data for representing Vidalia onions either quantitative or qualitative parameters. Therefore, our study not only represents an innovation in the field of specialty crop production, but also brings ready-to-use solutions to improve the production process and introduce Vidalia onions into the concept of field technology.

In the cultivation of fruit trees and vines, plant architecture is a critical determinant of productivity. While there are considerable diversities in plant architecture, which can be modified through pruning in fruit production, a method for high-throughput measurement and recording of architecture has not yet been established, posing a limitation to research and development in this area. Here we evaluated Transformer-based architecture for detecting above-ground shoot network of grapevine in an outdoor vineyard condition. The problem here was defined as the detection of nodes (buds or branching points) and their physical relationships (internodes or edges) within plant images. We also developed an evaluation metric inspired by the inherent structure of plant shoots to efficiently smooth detected structures to more closely resemble realistic systems in plants. The proposed framework has been successfully applied to the detection task in outdoor condition with complex background. Through the application of this method, we have demonstrated that our proposed framework is capable of extracting topological parameters of dormant shoot architecture of grapevine that effectively models the shoot biomass in a large-scale vineyard.

Downy mildew, caused by Hyaloperonospora parasitica, poses a significant threat to Brassica oleracea crops, leading to substantial reductions in yield and marketability. This study seeks to assess various vegetation indices for detecting different levels of downy mildew infection in a Brassica variety, Mildis, using hyperspectral data. Through artificial inoculation with H. parasitica sporangia suspension, distinct levels of downy mildew disease were induced. Spectral measurements, ranging from 350 nm to 1050 nm, were performed on the leaves under controlled environmental conditions, and reflectance data were collected and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculations were employed to identify the most informative wavelengths, which were then used to develop Downy Mildew Indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to assess downy mildew infection levels. The results revealed that a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% in distinguishing healthy leaves from those with early (DM1), progressed (DM2), and severe (DM3) infections, respectively, using the proposed downy mildew index. The development of this novel downy mildew index has the potential to facilitate the creation of an automated monitoring system for downy mildew and aid in resistance profiling in Brassica breeding lines.

Introduction: Fresh produce is commonly associated with foodborne disease outbreaks and food recalls. To prevent the lethal impact of outbreaks, effective traceability is crucial. Produce items are traditionally labeled with price lookup (PLU) stickers. However, those stickers are environmental hazards, and frequent detachment of PLU stickers losses the information for traceability. Purpose: To investigate the effect of postharvest quality and microbial safety of laser labeling on fresh produce. Methods: Three horticultural crops, apple ‘Red Delicious ‘apple, cucumber, and green bell pepper, were procured from a local grocery store. Each produce was printed with a Quick Response (QR) code or text code using the laser engraver machine, followed by the application of edible wax. All produce was stored at 4° C temperature and 90% relative humidity during the study period. The postharvest quality was measured through fresh weight loss, QR code readability, and visual quality for 16 days. In another study, the laser-labeled produce was assessed for microbial contamination by quantifying artificially inoculated rifampicin-resistant E.coli (ATCC 25922) at an initial concentration of 106 CFU/mL. The experiment had five treatments: QR-coded labels followed by waxing or no wax, text-coded labels followed by waxing or no wax, and nontreated control. Results: Fresh weight loss for laser-printed produce was slightly higher than the nontreated control, but no difference in visual quality ratings was observed compared to the control. The population of rifampicin-resistant E.coli was statistically higher in all three produce labeled with text code compared to the nontreated control. However, QR-coded treatments were similar in the control. The application of wax did not facilitate microbial attachment. Significance: Laser labeling technology did not deteriorate the postharvest quality and susceptibility to microbial contamination. Hence this technology has the potential in commercial application as a better alternative to the PLU sticker to improve traceability.

Cover crop recommendations can be complex based on regional factors and different growing conditions. In order to combat these challenges, the Precision Sustainable Agriculture team (PSA) developed online tools that are readily available for producers to help them optimize cover crops on their operation. Tools include a species and variety selector tool, seeding rate calculator, nitrogen calculator, and economic decisions tool. These platforms look to help producers find success with cover crops that fit their operation’s needs.

In South Korea, approximately 65% of the land is mountainous or forested, which limits large-scale farming. Over 53,000 ha of land has been reclaimed from the sea and dedicated to the development of large-scale indoor agricultural complexes. Given the coastal climatic conditions and flat nature, these areas present unique challenges including stronger winds and colder temperatures compared to the inland, leading to high air velocities and operation costs in naturally ventilated greenhouses. Aerodynamic analysis is necessary to estimate crop risk factors and identify potential aerodynamic problems before the construction of these structures. Traditional studies have focused on using natural ventilation rates to estimate greenhouse suitability for plant growth. However, under scenarios of high wind velocity, this approach has critical limitations in accounting for crop damage resulting from high air velocity induced inside naturally ventilated facilities. This is tailored to the fact that ventilation efficiency in naturally ventilated structures increases with an increase in air velocity. High wind velocity induced inside greenhouses is associated with rapid CO2 depletion, stomatal dysfunction, leaf abrasion, mechanical stress etc., which critically affect crop yield and biomass development. Under high wind environment, crop damage resulting from high internal air velocities is an important factor that needs to be accounted for during design of indoor agricultural facilities. This study introduces a CFD model for designing greenhouse complex including multiple greenhouses and model analysis approach. We developed the Aerodynamic Crop Damage Index (ACDI), used it to analyze the model, and compared it to the convectional ventilation efficiency approach. The ACDI revealed a 2.2-fold variation in damage potential based on the greenhouse's location within the complex and a 15-fold variation attributable to wind direction, pinpointing significant damage risks in zones with the highest and lowest air velocities. In contrast, the convectional ventilation efficiency method only identified damage risks in low-velocity areas. ACDI has not only the potential to account for high air velocity effects in naturally ventilated greenhouses but also presents an opportunity for specialized greenhouse complex control and management according to greenhouse position and incoming wind direction. Future research should aim at refining the ACDI for better aerodynamic analysis in greenhouse complexes planning and its integration into greenhouse ventilation control systems.

Acknowledgments: This work was supported by “Regional Innovations Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by Ministry of Education (MOE) (2024RIS-008)

This year Conviron is launching three new products:
•             GEN1000-ECO (introduction date: April 16, 2024)
•             ConvironDirect (introduction date: March 4, 2024)
•             PGR15/E15 LED Retrofit (introduction date: Jan 5, 2024)
GEN-1000-ECO:
The GEN1000-ECO is a new compact reach in chamber ideal for short and tall plant research that comes standard with humidity control and energy efficient features such as a smaller compressor and LED lighting - for up to 15% reduced energy consumption. Low, medium and high light options are available to meet a range of research requirements.
ConvironDirect:
ConvironDirect is a new premium software tool that enables users to manage chamber setpoints and actual conditions remotely through any building LAN connected desktop, notebook or handheld mobile device. ConvironDirect is ideal for users that have Conviron reach-in plant growth chambers or walk-in rooms and want a seamless connection to their chamber, their plants, and their data from virtually anywhere.
PGR15/E15 LED Retrofit:
Fluorescent lamps such as T5, T8 and T12 have been the standard for many years and have been used in tens of thousands of plant growth chambers around the world. However, fluorescent lighting is trending towards obsolescence and replacement lights are increasingly difficult to source economically. Conviron is now offering a retrofit for aged PGR15-E15 chambers to enable users to take advantage of the latest LED lighting technology and save up to 80% on energy costs.

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.

Development of Crop Water Stress Index for Hazelnuts in the Willamette Valley Using Distributed Low-Cost Infrared Thermometers - Dalyn McCauley
Performance Evaluation of a Robust Chip-based RF sensor for Soil Moisture Determination - Jannatul Ferdaous Progga
Development and Demonstration of a Sensor-Based Method for Monitoring Container Substrate Fertility - Michelle Ezequelle
Enhancement of air quality in agricultural facility using particulate matter reduction systems - hyojae Seo
Experience with New Generation Horticultural Education, Research and Production Through Indoor Ag. - Bipul Biswas

Incorporating data-driven technologies into agriculture offers an effective strategy for optimizing crop production, particularly in regions reliant on irrigation. This becomes increasingly crucial in the face of escalating heatwaves and droughts associated with climate change. Recent advancements in sensor technologies have spawned various methods for assessing irrigation needs. Notably, infrared thermometry stands out as a non-destructive remote sensing method capable of monitoring transpiration, holding significant potential for integration into drone- or satellite-based remote sensing models. This study focuses on the application of infrared thermometry to develop a Crop Water Stress Index (CWSI) model for European hazelnuts (Corylus avellana), a significant crop in Oregon, the leading hazelnut-producing state in the United States. Using low-cost open-source infrared thermometers and data loggers, this research aims to provide hazelnut farmers with a practical tool for monitoring crop water status, improving irrigation efficiency, and ultimately enhancing hazelnut yields. The study, spanning from June to September 2021 in a ‘Jefferson’ hazelnut (Corylus avellana) orchard, applied three distinct irrigation treatments. The calibration of the low-cost IRT sensors achieved a high accuracy (R² = 0.99), validating their utility in detecting variations in canopy temperature consistent with irrigation treatments. The developed CWSI is well-correlated with traditional plant water status indicators including stem water potential, leaf conductance, and transpiration. These results demonstrate the potential of this model to accurately reflect physiological symptoms of water stress in hazelnuts. This research not only introduces a novel CWSI model tailored to hazelnuts but also underscores the utility of low-cost technology in enhancing agricultural monitoring and decision-making.

Controlling water cycles, anticipating disasters, and enhancing agriculture depends on accurate soil moisture understanding. To address climate-related challenges, precise and real-time measurements from soil moisture sensors are essential. Radio Frequency (RF) soil moisture sensors are wireless, low-cost, and simple devices that revolutionize agriculture with real-time accuracy, advance environmental science, and promote sustainable resource management. This study aims to calibrate an innovative chip-based RF sensor using the gravimetric method for moisture content detection. Sensor calibration will be performed for sandy and loamy soils, as varying soil types affect the dielectric constant and complex permittivity measured by RF sensors. The project will explore linear and polynomial regression machine learning techniques to improve the accuracy, efficiency, and reliability of the calibration curves. A pot test with sandy and loamy soils will validate the sensor for moisture content monitoring by comparing it with a commercial moisture content device. The detection range of the sensor is calibrated and validated up to 35% moisture content. This research can demonstrate the accuracy, simplicity, affordability, and robustness of the chip-based RF sensor for soil moisture detection, contributing to the improvement of precision agricultural enhancements.

Substrate electrical conductivity (EC) measurement is a required Best Management Practice (BMP) for the application of supplemental fertilizers in Florida nursery and greenhouse industries to protect and conserve water resources. The current method of measuring substrate EC is through the Pour-through (PT) procedure, a multi-step method in which representative plants are selected for EC measurement, and a predetermined volume of water is poured on the surface of each test plant. The resulting leachate is collected and EC is determined using an EC meter. This process can be extensive for large-scale nursery production zones, requiring a significant amount of time and manual labor. With the personnel shortages that exist in production nurseries, technologies are needed to improve and optimize EC measurement and recordkeeping so the BMP is effective. This project aims to develop a new, sensor-based method for measuring EC to reduce the time invested by producers compared to the current PT method and provide real-time information on the fertility status of container-grown plants. To achieve this goal, a variety of low-cost, soil-based EC sensors were selected for measuring container substrate EC. Laboratory tests were conducted to evaluate the impact of various environmental parameters on sensor performance and select an optimal sensor for use in this application. A sensor system was designed for field deployment and wireless communication was established to monitor sensor data remotely. A field study is currently being conducted to compare EC data obtained from the sensors to EC measurements collected manually using the PT procedure and develop a protocol for sensor deployment in nurseries. At the end of the experiment, a destructive soil sampling technique will be employed to examine salt stratification within the nursery containers and help determine optimal sensor placement in the pots. This study highlights the need for technology and data-driven methods in modern agricultural practices to address challenges such as production efficiency and personnel shortages.

In agricultural facility, which are equipped with mechanical and closed ventilation systems, have faced the challenge to reduce fine dust concentration for enhancing working environment. Among the dust sources, fruit fuzz, characterized by its dense and needle-like structure, can induce allergic symptoms in agricultural workers upon exposure to their respiratory systems and skin, adversely impacting their health and deteriorating the work environment. The focus of this research is the development of a fine dust reduction system aimed at enhancing the working conditions. The system operates by generating a downward airflow to prevent fine dust from reaching the workers' respiratory systems. To assess the efficacy of the fine dust reduction system, real-time measurements of dust concentrations were conducted at commercial peach sorting stations, both before and after the operation of this system. The findings revealed that during peach sorting task, the total dust concentration was 6.89 times higher than the normal condition, representing the critical need for reducing fine dust levels. The deployment of a particulate matter reduction system specifically within the fruit sorting area, a section identified for substantial dust generation due to the removal process of fruit covering bags, has led to a substantial decrease in airborne particulate concentrations. This targeted intervention resulted in an 80.4% reduction in Total Suspended Particulate (TSP) levels and a 60.3% decrease in PM-10 concentrations at the site of implementation. Additionally, a broader assessment across the entire sorting facility revealed a significant decline in fine dust levels, with TSP concentrations diminishing by 67.6% and PM-10 concentrations by 52.2%. This research underscores the efficacy of targeted fine dust control measures within agricultural facilities, markedly enhancing air quality and the occupational environment for agricultural laborers.

These days it has become almost impossible to depend on climate for agricultural production of any crops mainly horticultural crops. Unpredictable climate conditions have been a significant challenge to growers. Therefore, it is an urgent need for horticultural educators, researchers, and growers to come up with new approach to explore new farming techniques. This abstract is to discuss over 8 years experiences of research and education on Indoor Ag includes hydroponics with vertical, horizontal, fully automated, or partially automated farming techniques. It has enormous potential to overcome all challenges that is claimed to grow plants and global food security due to population growth, unpredictable climate, water scarcity, space, labor, and food safety related. Indoor Ag is mainly soilless, it is controlled environment agriculture (CEA). Opening educating opportunities to new generation who can come up with new innovative designs with new techniques to improve it for better. In recent times Indoor Ag has come up with very high expectation, and capable of growing plants from several hundred times more than traditional farming per year. Besides, Indoor Ag (IA) facility or controlled environment agriculture could produce the best quality crops. With the experiences in Indoor Ag along with traditional outdoor Ag, the conclusion is we need to develop education, research, and extension curricula about Indoor Ag, urgently. Indoor Ag as a new discipline it has a few challenges but could be overcome easily by our intelligent next generation students. They can take Indoor Ag education, research and production techniques as the future Horticulture. At present, globally a limited number of faculties and researchers has been involved that needed to be increased through interest and hands-on training in this new technology. It has been observed, most of the Indoor Ag is run by business owners and for business secret they cannot share their true success story to increase competition that we all agree. But we researchers who have been working for the better future to overcome multifaceted challenges can see the Indoor Ag as potential alternative. Therefore, now is the time we should adopt Horticultural education, research, and production through Indoor Ag. We need to develop academic courses, education, and research activities from K-12 to undergraduate and graduate programs in college and Universities. So, whoever involved in agricultural research and education at this moment Indoor Ag should be our goal to make it future global horticulture education, research, and production method.

A forum for discussion of potential collaborations with regards to technology in horticulture – i.e. biotechnology, UAVs, cameras, sensors, artificial intelligence, etc.

As Artificial Intelligence (AI) continues development at a rapid pace, our current teaching and learning methods are also swiftly transforming. AI itself is often combined with various technologies such as image recognition, virtual reality (VR), machine learning, adaptive learning algorithms, and gamification. With the merger of existing technology, AI and education will change the way we teach as well as how students learn. Some examples for teaching Horticulture, Landscape Architecture or Plant Science include individualized teaching, deep learning, adaptive learning environments, AI-based assessment and image recognition. In this Professional Interest Group Session speakers will provide examples of how they are using AI in their teaching methods, followed by an open discussion with the audience that should provide additional examples and applications.

Coordinator(s)

• Kathryn Orvis, Purdue Univ, Horticulture and Landscape Architecture, West Lafayette, Indiana, United States

Speaker/Participant(s)

• Mary Rogers, University of Minnesota, Department of Horticultural Science, St Paul, Minnesota, United States
  How to Incorporate Generative AI in Teaching a Writing Intensive Urban Agriculture Course (15 mins)
  Summary:
• Aaron Thompson, Purdue University, Horticulture and Landscape Architecture, West Lafayette, IN, United States
  Teaching with AI in Landscape Architecture (15 mins)
  Summary:
• Cynthia Haynes, Iowa State University, Horticulture, Ames, Iowa, United States
  Potential benefits and pitfalls of using AI software in Horticulture teaching. (15 mins)
  Summary: