A Remote Strawberry Health Monitoring System Performed with Multiple Sensors Approach

Xiao Du, Jun Steed Huang, Qian Shi, Tongge Li, Yanfei Wang, Haodong Liu, Zhaoyuan Zhang, Ni Yu and Ning Yang ()
Additional contact information
Xiao Du: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Jun Steed Huang: Institute of Electrical and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
Qian Shi: College of Engineering, China Agricultural University, Beijing 100083, China
Tongge Li: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yanfei Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Haodong Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Zhaoyuan Zhang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Ni Yu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Ning Yang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 15, 1-17

Abstract: Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation.

Keywords: health monitoring; IoT-based system; adaptive screening; leaf soft-measurement; thermal infrared imaging (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/15/1690/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/15/1690/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:15:p:1690-:d:1718080

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().