Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases transmitted by mosquitoes are expanding beyond tropical regions due to climate change. Agricultural pests further exacerbate economic losses by damaging crops. The Sterile Insect Technique (SIT) emerges as an 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 in the Valencian Community, Spain, funded by the Conselleria de Agricultura, Agua, Ganaderia y 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. Also, our device can analyze multiple ovitraps simultaneously without the need of manual replacement. This approach significantly enhances the accuracy of egg counting and classification, providing a valuable tool for large-scale field studies. This document describes part of the work of the project Application of Industry 4.0 techniques to the production of tiger mosquitoes for the Sterile Insect Technique (MoTIA2,IMDEEA/2022/70), financed by the Valencian Institute for Business Competitiveness (IVACE) and the FEDER funds. The participation of J.Naranjo-Alcazar, J.Grau-Haro and P.Zuccarello has been possible thanks to funding from IVACE and FEDER funds. The participation of D.Almenar has been financed by the Conselleria de Agricultura, Agua, Ganaderia y Pesca of the Generalitat Valenciana and the Subdireccion de Innovacion y Desarrollo de Servicios (TRAGSA group).

翻译：害虫防治是一个全球性挑战，影响着公共卫生、食品安全和环境。由于气候变化，蚊媒疾病正蔓延至热带以外地区。农业害虫进一步加剧了作物损失，导致经济损失。昆虫不育技术（SIT）作为化学农药的环保替代方案应运而生，该技术通过对雄性昆虫进行绝育并释放以抑制种群增长。本研究聚焦于自动化分析用于跟踪西班牙瓦伦西亚社区白纹伊蚊SIT项目的野外诱蚊器，该项目由农业、水利、畜牧与渔业委员会资助。先前研究已利用深度学习算法实现诱蚊器中蚊卵的自动计数，但仍面临人工操作和分析能力有限等挑战。本研究的创新点包括：将蚊卵分类为已孵化与未孵化状态，以及通过部分图像重建完整诱蚊器图像，从而有效解决重复计数和卵粒切割问题。此外，我们的设备能够同时分析多个诱蚊器而无需人工更换。该方法显著提升了蚊卵计数与分类的准确性，为大规模野外研究提供了重要工具。本文描述了"工业4.0技术在不育昆虫技术虎蚊生产中的应用"项目（MoTIA2,IMDEEA/2022/70）的部分工作，该项目由瓦伦西亚企业竞争力研究所和欧洲区域发展基金资助。J.Naranjo-Alcazar、J.Grau-Haro和P.Zuccarello的参与得益于IVACE和FEDER基金的支持。D.Almenar的参与由瓦伦西亚大区政府农业、水利、畜牧与渔业委员会及创新与服务开发副总监办公室（TRAGSA集团）资助。