Data Analytics-Driven Precision Horticulture Engineering


Manfrini Luigi Manfrini

Luigi Manfrini

University of Bologna, Italy

Manuela Manuela Zude-Sasse

Manuela Zude-Sasse

Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany

Lav Lav Khot

Lav Khot

Washington State University, USA

Mengoli Dario Mengoli

Dario Mengoli

University of Bologna, Italy


This session captures the latest methodological developments and applications in precision horticulture engineering. Research related to smart technologies that aid in fine-tuning crop production as well as postharvest practices are welcome. Specific focus is given to technologies for site-specific weather and crop monitoring driven decision support tools, controlling light use, irrigation, nutrients, crop protection, crop load, and harvest management. Furthermore, precise monitoring and measures considering preharvest factors affecting the postharvest fruit and vegetables quality and storability are invited. Papers that present specific smart sensor and actuation technologies, artificial intelligence (AI)/machine learning (ML) enabled edge and cloud computing solutions will be considered as well.


We welcome contributions that cover the following topics:

  • Crop stress sensing- both in open and protected environments with real-time decision support by harnessing Internet-of-Things concepts, network connectivity, edge/cloud computing with embedded AI/ML models
  • Monitoring and using data on preharvest factors affecting postharvest properties of produce
  • Integration of data, mechanistic models, and AL/ML models for decision support
  • Learnings from pilot/demo farms, testbeds that explore farms of the future concepts
  • Optimizing light, water, nutrients, and pesticide use efficiency by means of plant data
  • High throughput phenotyping aimed at optimized breeding and translational technology in horticultural crops


Luigi Manfrini, an associate professor at the Agricultural and Food Sciences Department of the University of Bologna, directs his research towards the integration of new technologies and precision management methodologies, examining their impact on fruit tree physiology within varying environmental contexts. His overarching goal is to devise innovative strategies that enhance orchard sustainability while upholding superior quality and yields. Currently, he is actively engaged in regional, national, and international initiatives, focusing on precision management implementation, sustainable fruit production, and optimizing resource utilization. Additionally, Luigi serves as the chair of the Working Group on Mechanization, Digitization, Sensing, and Robotics at the International Society for Horticultural Science, and fulfills the role of secretary for the EUFRIN Working Group on Digital Orchards. With a portfolio boasting over 100 publications across scientific and professional journals, Luigi obtained his PhD in precise orchard management in 2009 and holds a Master of Science degree in Agricultural Sciences and Technologies since 2004.

Manuela Zude-Sasse earned her PhD from the Technical University Berlin, later assuming the role of Associate Professor at Humboldt University after receiving habilitation in "Applied Plant Physiology." Subsequently, she became a Professor at the Berlin University of Applied Sciences, Germany. Currently, she leads the PRECISION HORTICULTURE group at the Leibniz Institute for Agricultural Engineering and Bioeconomy. Throughout her career, she has authored over 80 papers indexed in Web of Science (WoS), curated several (special) issues for international journals, and co-authored a book on optical methods for crop sensing. Manuela's research focuses on sensor development and translating signals into actionable plant information, which finds practical application in agronomic processes.

Lav R. Khot is the Associate Professor and Director of AgWeatherNet, Washington State University (WSU). Dr. Khot directs one of the largest Agricultural Weather Network in the U.S. and precision agriculture research and extension program at WSU with focus on developing site-specific crop monitoring and management technologies through crop, environmental sensing, and automation. These translational efforts help ensure optimal use of crop input resources, such as chemicals, water, energy, and labor, as well as improved produce quality. He is recipient of ‘Fruit + Vegetable 40 Under 40’ from Fruit Growers News, 2021 and ‘2018 New Innovator in Food and Agriculture Research’ Award from Foundation for Food and Agriculture Research. He has published over 110 peer-reviewed papers in this area and over 350 combined national and international conference talks, extension/outreach workshops and short courses. He currently serves as the Associate Editor for American Society of Agricultural and Biological Engineers (ASABE) Transactions. He also chaired the ‘Mechanization, Digitization, Sensing and Robotics Workgroup’ of ISHS-International Society of Horticultural Science (2018-2023) and a multi-state group (S1069: Research and Extension for Unmanned Aircraft Systems (UAS) Applications in U.S. Agriculture and Natural Resources, 2021-2022).

Dario Mengoli presently serves as an assistant professor at the Department of Electrical, Electronic, and Information Engineering at the University of Bologna. He earned his master’s degree in computer science engineering from the same institution in 2008. Subsequently, he pursued a career as a freelance consultant until 2019, concurrently engaging in diverse research endeavors focused on robotics and agricultural task automation.
Dario's primary research interests encompass autonomous navigation, prototype development, machine learning, and automation, with a specific emphasis on mobile ground and aerial robotic platforms, as well as software development. Currently, he is deeply immersed in an innovative orchard project, backed by the Italian Ministry of Research funding programme for departments of excellence. The project aims to develop a dependable sprayer and mulcher robot to integrate into a novel apple production cultivar concept.
Furthermore, Dario has actively explored emerging machine learning techniques, particularly in applying artificial intelligence and image classification algorithms to address agricultural challenges.