Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Next Article in Journal
Tourist Attractiveness of Rural Areas as a Determinant of the Implementation of Social Tourism of Disadvantaged Groups: Evidence from Poland and the Czech Republic
Previous Article in Journal
Design of Rice Straw Fiber Crusher and Evaluation of Fiber Quality
Previous Article in Special Issue
Robust Multi-Gateway Authentication Scheme for Agriculture Wireless Sensor Network in Society 5.0 Smart Communities
Article

VineInspector: The Vineyard Assistant

1
Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
2
CITAB—Centre for the Research and Technology of Agro-Environment and Biological Sciences, UTAD—University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
3
INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Pólo da FEUP, Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal
4
INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Pólo da UTAD, UTAD—University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Xiuliang Jin
Agriculture 2022, 12(5), 730; https://doi.org/10.3390/agriculture12050730
Received: 26 April 2022 / Revised: 18 May 2022 / Accepted: 18 May 2022 / Published: 22 May 2022
(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)
Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants’ phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches’ accuracy. Two applications were developed to evaluate VineInspector’s consistency while a viticulturist’ assistant in everyday practices. One was intended to determine the size of the very first grapevines’ shoots, one of the required parameters of the well known 3–10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard’s phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases. View Full-Text
Keywords: precision viticulture; grapevine downy mildew; pest count; Scaled-YOLOv4; Internet of Things precision viticulture; grapevine downy mildew; pest count; Scaled-YOLOv4; Internet of Things
Show Figures

Figure 1

MDPI and ACS Style

Mendes, J.; Peres, E.; Neves dos Santos, F.; Silva, N.; Silva, R.; Sousa, J.J.; Cortez, I.; Morais, R. VineInspector: The Vineyard Assistant. Agriculture 2022, 12, 730. https://doi.org/10.3390/agriculture12050730

AMA Style

Mendes J, Peres E, Neves dos Santos F, Silva N, Silva R, Sousa JJ, Cortez I, Morais R. VineInspector: The Vineyard Assistant. Agriculture. 2022; 12(5):730. https://doi.org/10.3390/agriculture12050730

Chicago/Turabian Style

Mendes, Jorge, Emanuel Peres, Filipe Neves dos Santos, Nuno Silva, Renato Silva, Joaquim J. Sousa, Isabel Cortez, and Raul Morais. 2022. "VineInspector: The Vineyard Assistant" Agriculture 12, no. 5: 730. https://doi.org/10.3390/agriculture12050730

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop