Pest detection based on deep learning techniques and population estimation in greenhouses

Greenhouse cultivation has a significant activity area in Turkey, particularly in the Mediterranean and Aegean regions. The products produced in greenhouses incur losses due to diseases, pests, nutritional inadequacy, misused pesticides, or improper storage. Agricultural pests, can be considered one of the most significant economic and productivity losses for greenhouse products. Detection of thrips at the preliminary stages, which is endemic in greenhouses, has immense importance. Frankliniella occidentalis, a species belonging to the thrips family, was discussed in this thesis. To observe thrips population growth, the pests on the traps are counted weekly with relevant equipment. In this respect, this process requires high labour and time. In the thesis, counting pests by using deep learning-based algorithms instead of a human factor, was aimed. Within this scope, yellow and blue sticky trap images in greenhouses in Antalya were obtained. Also, collection date from the greenhouses, temperature and humidity information of the greenhouses were obtained. The collected images were divided and a dataset was developed. This was trained with 4 different state-of-the-art models. YOLOv7 with DarkNet53 backbone was the most successful model with an AP50 value of 0.889. The collected numerical data were trained with 4 different machine-learning models. In the thesis, in which time series were used as a feature, the linear regression model gave the most successful result. A trend analysis was performed using this model and the population was estimated. An interface is designed to inform the user about the pest situation in the greenhouse.