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A Remote Strawberry Health Monitoring System Performed with Multiple Sensors Approach

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiao Du & Jun Steed Huang & Qian Shi & Tongge Li & Yanfei Wang & Haodong Liu & Zhaoyuan Zhang & Ni Yu & Ning Yang, 2025. "A Remote Strawberry Health Monitoring System Performed with Multiple Sensors Approach," Agriculture, MDPI, vol. 15(15), pages 1-17, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1690-:d:1718080
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