| Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases like dengue, malaria, and Zika, transmitted by mosquitoes, are expanding beyond tropical regions due to climate change. The Sterile Insect Technique (SIT) emerges as a promising, eco-friendly alternative to chemical pesticides, involving the sterilization and release of male insects to curb population growth. This work focuses on the automation of the analysis of field ovitraps used to follow-up a SIT program for the aedes albopictus mosquito species in the Valencian Community, Spain, led and funded by the Conselleria d'Agricultura, Aigua, Ramaderia i Pesca. Previous research has leveraged deep learning algorithms to automate egg counting in ovitraps, yet faced challenges such as manual handling and limited analysis capacity. Innovations in our study include classifying eggs as hatched or unhatched and reconstructing ovitraps from partial images, mitigating issues of duplicity and cut eggs. This approach enhances the accuracy and efficiency of egg counting and classification, providing a valuable tool for field studies of SIT programs. The proposed system achieves an F1-Score of 93.5% across all classes demonstrating robustness and reliability. |
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