Work

Development of an Internet of Things (IoT)-based disease forecaster to manage purple spot on asparagus fern

Year: 2025

Type: article

Abstract: Stemphylium vesicarium causes purple spot disease on asparagus spears rendering them unmarketable. The pathogen also infects the asparagus fern, causing premature defoliation , impacting subsequent yields. Foliar disease on the fern is managed with fungicides which can be applied according to TOMCAST (TOMato disease foreCASTing) based on disease severity values (DSV) or a calendar-based schedule. Leaf wetness sensors play an important role in generating DSV but are not standardized. We assessed disease control when fungicides (azoxystrobin alternated with chlorothalonil) were applied according to TOMCAST using SpecConnect or the Internet of Things (IoT)-based LOCO-DM (Low-Cost sensor monitoring system for Disease Management) at two thresholds (15 or 20 DSV) or every 10 days. Weather data to determine the DSV were generated and compared using SpecConnect and LOCO-DM. The METER Group PHYTOS 31 sensor used in LOCO-DM provided more accurate results compared to the SpecConnect. In 2022, the SpecConnect model and LOCO-DM generated a season total of 113 and 109 DSV, respectively. In 2023, the 10-day treatment received 8 applications, the SpecConnect TOMCAST 15 and 20 DSV treatment received 6 and 4 applications, respectively. The LOCO-DM TOMCAST 15 and 20 DSV received 6 and 5 applications, respectively. Only the 10-day and LOCO-DM 15 DSV had a significantly lower final disease assessment than the non-treated control. Area under disease progress curve (AUDPC) data indicated that all treatments limited disease compared to the non-treated control. The final disease assessment and AUDPC values were similar between intervals applied using SpecConnect and LOCO-DM. The IoT based LOCO-DM can be used as an accessible way to advance disease forecasting so that fungicides are applied only when the risk of crop infection is high which may reduce disease management costs and environmental exposure without sacrificing control.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.100941

Authors: John R. Spafford, Mary K. Hausbeck, Benjamin P. Werling, Stewart F. Tucker, Younsuk Dong

Institution: Michigan State University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,770