Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves
| Autor: | Christos Alexakos, Aris S. Lalos, A. Makedonas, Sofoklis Katakis, Christos Theoharatos, Christos Xouris, Kostas Blekos, Anastasios Tsakas, Dimitris Alexandropoulos, Ioannis Evdokidis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Control and Optimization 010504 meteorology & atmospheric sciences Computer Networks and Communications Computer science Multispectral image 0211 other engineering and technologies Multi spectral Automatic processing multi-spectral sensing 02 engineering and technology Agricultural engineering 01 natural sciences lcsh:Technology Instrumentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences multi-spectral fusion of multispectral spectroscopy data precision agriculture lcsh:T Monitoring system multi-spectral co-registration Olive trees intelligent management Precision agriculture Verticillium wilt |
| Zdroj: | Journal of Sensor and Actuator Networks, Vol 10, Iss 15, p 15 (2021) Journal of Sensor and Actuator Networks Volume 10 Issue 1 |
| ISSN: | 2224-2708 |
| Popis: | The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) and the application of beneficial microorganisms (Micosat F BS WP) restore health in infected trees. However, for their efficient implementation the above methodologies require the marking of trees in the early stages of infestation—a task that is impractical with traditional means (manual labor) but also very difficult, as early stages are difficult to perceive with the naked eye. In this paper, we present the results of the My Olive Grove Coach (MyOGC) project, which used multispectral imaging from unmanned aerial vehicles to develop an olive grove monitoring system based on the autonomous and automatic processing of the multispectral images using computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, help in the prediction of Verticillium wilt spread and implement a decision support system that guides the farmer/agronomist. |
| Databáze: | OpenAIRE |
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