Evaluating intelligent sprayers and warning systems for apple IPM in Iowa

Thumbnail Image
Meyer, Olivia Kathleen
Major Professor
Gleason, Mark L
Nonnecke, Gail R
Slack, Suzanne M
Iles, Laura C
Nair, Ajay
Committee Member
Journal Title
Journal ISSN
Volume Title
Research Projects
Organizational Units
Journal Issue
Is Version Of
Apples are an economically valuable fruit crop in the United States. However, managing pests and diseases that target apples is costly and can pose health and environmental hazards. Current chemical management strategies include using air-assisted (airblast) sprayers to apply pesticides and calendar-based spray schedules to time pesticide applications. Airblast sprayers do not target pesticide sprays precisely and waste a large percentage of pesticides during off-target movement. Calendar-based spray schedules do not account for environmental conditions to predict disease outbreaks, which can lead to inefficient application timing. A modified airblast sprayer called an Intelligent Sprayer uses Light Detection and Ranging (LiDAR) technology to target pesticide sprays more precisely, and weather-based warning systems have been developed to predict the risk of diseases that affect apples in the Midwest (e.g., sooty blotch and flyspeck (SBFS) and fire blight). Our 3-year (2020-2022) project at the Iowa State University Horticulture Research Station (ISUHRS) tested the efficacy of combining Intelligent Sprayer technology with the SBFS and fire blight disease-warning systems to reduce the pesticide load on apples during the growing season. The objectives were to evaluate the efficacy of traditional and modern management strategies for differences in the volume of pesticide applied during each spray, the number of sprays in each growing season, spray coverage using different application rates, and incidence of disease damage. Compared to the standard airblast sprayer rate, pesticide volume per growing season used by the Intelligent Sprayer was 45% lower on average using its low-rate application setting and 55% lower on average using its high-rate application setting. The SBFS warning system saved four fungicide sprays each year, and the MaryblytTM warning system saved one bactericide spray in 2021, compared to a calendar-based spray schedule. SBFS colonies did not appear in any treatment, and fire blight symptoms were also absent. Comparison of spray coverage percentages showed no significant differences between sprayer types and rates at all three growth stages. The Intelligent Sprayer at a high rate (0.09 fl oz/ft3) produced better uniformity than the Intelligent Sprayer low rate (0.09 fl oz/ft3) and that standard airblast rate (100 GPA) in spray coverage at two of the three growth stages. Incidence of pest and disease damage on harvested fruit using the Intelligent Sprayer, the warning systems, or both tactics together did not differ significantly from that of control treatments (standard airblast sprayer and calendar-based pesticide-spray timing). This field experiment addressed limitations in the efficiency of using standard management practices in apple production in the Midwest. My experiment was the first replicated trial to examine the effectiveness of using both the Intelligent Sprayer technology and disease warning-systems as more efficient practices for pest and disease control. The final objective of my thesis was to develop a case study to advance the understanding of applied plant health science in a college or university setting. The case study highlighted public concerns about the use of pesticides for in apple orchards and the potential value of new management strategies such as the Intelligent Sprayer. This case was tested in an undergraduate plant pathology course (PLP 408/508) at Iowa State University and submitted for peer review to the American Phytopathology Society’s open-access online journal The Plant Health Instructor, where it was accepted following revision and published.
Subject Categories