Turkey is one of the largest sources of olives in the world. In more recent decades, climate change has caused a rise in floods, droughts, and pests that have posed a challenge to the olive industry. These issues are roadblocks for researchers as well, who try to predict when these disasters occur to prevent destruction.
To combat this issue and support research, a team of students from Istanbul Technical University ARIS LAB and AKAL LAB and the IEEE Signal Processing Society have collaborated with The Nesin Matematik Köyü (Nesin Mathematics Village) to create a project that will monitor olive trees and establish a warning system for farmers.
This project has two parts. The first is to develop a deep learning technique to detect changes in olive trees as well as olive fruits over the period of the project to identify any irregularities or abnormal growth such as flower-loss or fruit rottenness and act as an early warning system for farmers and owners to take all necessary measures to avoid low or damaged harvest
The second part is to ensure the longevity of the project and establish a cloud of open-source data that will allow farmers and researchers to access the data and use it to their advantage.
This device will use imaging systems that will alleviate duties that traditionally fall to farmers. Optical, thermal, and hyperspectral imaging are used to monitor olive trees, especially their health.
This system also operates with aerial drones to conduct observations on larger scales and harder to reach areas.
The data collected for the cloud-based system will be analyzed and used to improve manners of detection. The image systems will provide more detailed and accurate information than was previously possible, allowing farmers to better protect their crops.
The team also hopes to, in conjunction with the NGO, raise awareness about the issues that olive farmers face due to climate change.
This project was made possible by a $9952.69 grant from EPICS in IEEE.