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Project

PhD position – Efficient Deep Learning Strategies for Image-Based Insect Recognition

According to FAO estimates, pests cause up to 40% of the world's crop yield to be lost each year. Over $70 billion is lost annually to invasive insects in the global economy. On a global scale, invasive species have a significant role in ecosystem services deterioration, biodiversity loss, and ecological degradation. A sensor system that allows for the rapid identification of flying insects is currently lacking, and identification of insects is mainly based on counting insects on specialized traps. This procedure is subjective and time consuming. As a result, it is only performed on a weekly basis, and at a limited number of locations. Therefore, a rapid system for insect detection would be highly valuable. In the MeBioS division, basic research has been conducted to develop insect recognition sensor devices. We specialize primarily on image and wingbeat signature analysis, with an emphasis on insect pests in the fruit and vegetable producing industry. To this end, we collaborate with several partners in Flanders and abroad to bring together all required expertise. We have already demonstrated that similar species may be identified under laboratory conditions. Now, we want to conduct research on (1) more advanced setups that could potentially improve classification results; (2) robust systems, both from a hardware and software perspective, that enable accurate measurements in the field; (3) data analytics that combine information from multiple sources; and (4) the foundation for a remotely accessible sensor network.

Date:15 Nov 2022 →  Today
Keywords:insect
Disciplines:Machine learning and decision making
Project type:PhD project