Monitoring animal populations has been a long-standing challenge in wildlife ecology. One focus of the Arava-Dead Sea Science Center (ADSSC) is monitoring jackal and fox populations as agricultural zones expand. Researchers at the ADSSC use camera traps for ecological monitoring, and manually sort the images into categories. This process takes at least 12 hours of work per week. To reduce the workload of researchers at the ADSSC, their image sorting process was automated using machine learning and integrated into the current workflow. Over 90% accuracy in classifying “jackals”, “foxes”, “other”, and “empty” was achieved using our machine learning algorithm. A comprehensive user manual was created to allow researchers at the ADSSC and future users to utilize the software with ease.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Hall, Liam
• Carleu, Lillian
• Reiss, Jacob
• Rockmael, Jason

Subject

• Industry
• Project-Based Learning
• Sustainability
• Computing
• Environment
• Innovation

Publisher

• Worcester Polytechnic Institute

Identifier

• 89726
• E-project-022723-081145

Advisor

• Bar-On, Isa
• Solovey, Erin

Year

• 2023

Center

• Israel Project Center (Eilat) - IQP

Sponsor

• Dr. Nitzan Segev, Arava-Dead Sea Science Center

UN Sustainable Development Goals

• 9 - Industry, Innovation and Infrastructure

Date created

• 2023-02-27

Resource type

• Interactive Qualifying Project

Source

• E-project-022723-081145

Rights statement

• In Copyright

Dernière modification

• 2023-06-08