סמינר מחלקה של יסמין מזר - זיהוי מחלת קסיללה בעצי שקד מצילומי רחפן
School of Mechanical Engineering Seminar
Monday April 17.4.2023 at 14:00
Wolfson Building of Mechanical Engineering, Room 206
Xylella Diseases detection in orchards grown under nets using UAV-based images
Tel Aviv University, Institute of Agricultural Engineering, ARO
Supervisors: Dr. Victor Alchanati, Dr.Avishai Sintov
Almond trees are infected by the bacterium Xylella fastidious and the pathogen is vectored by xylem-
feeding sharpshooters and spittlebugs. Currently, no effective management techniques prevent trees
from becoming infected. The Xylella bacterium is introduced into the tip tubes of the tree by a tip-
sucking insect. As a result, there is a disruption in the fluid passage, and eventually, the tree dies. The
almond leaf scorch disease (ALS) was first identified in Israel In 2016 in the almond groves in the north
of the country. The bacterium poses a significant threat to almonds and other crops in Israel, such as
vines, olives, deciduous trees, and citrus fruits. Symptomatic trees that did not die in the previous
season will grow asymptomatic leaves in the following season, but with entering the summer months
the symptoms of the disease will reappear.
Remote sensing is a type of geospatial technology that samples emitted and reflected
electromagnetic (EM) radiation from the Earth’s terrestrial, atmospheric, and aquatic ecosystems
aiming to detect and monitor the area component without any physical contact. The data is collected
by aircraft-based and satellite-based sensor technologies, either passive or active Passive sensors,
which are the sensors that are used in this research, respond to external stimuli, gathering radiation
that is reflected or emitted by an object or the surrounding space. Popular examples of passive
remote sensors include charge-coupled devices, film photography, radiometers, and infrared. Active
sensors use internal stimuli to collect data, emitting energy to scan objects and areas at which point a
sensor measures the energy reflected from the target.
The importance of using remote sensing can be manifested when collecting data from dangerous or
inaccessible areas, with growing relevance in modern society.
Another advantage is that collecting data with remote sensing tools provides fast and repetitive
coverage of extremely large areas for everyday applications.
Front-line remote sensing tools, coupled with artificial intelligence (AI) technologies, have a significant
role in crop monitoring and disease surveillance. Crop-type classification is an example of using
remote sensing to provide precise, timely, and cost-effective information at different spatial,
temporal, and spectral resolutions.
Today, expert agronomists detect the XF disease by scouting. Each orchard is large, e.g, Sde
Nehemiah Almond orchard is about 16 ha.
As a result of the large area it is very hard for the scout to reach optimal detection of the infected
trees, he can even miss infected trees and as a result, the Xylella pathogen will spread widely in the
orchards. The long-term goal of this research is to develop a system that detects and locates infected
trees in an orchard and directs the scout to that trees without any need to search each tree whether
it is infected or not.
The objective of this work was to develop machine learning and classification algorithms that detect
and locate almond trees infected by Xyllela, using images acquired by a UAV.
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