Convolutional Neural Network Modeling for Pest Detection in Corn Crops: Optimization for Monitoring Efficiency

This project aims to develop automated techniques for early detection of pests in maize crops using computer vision and deep learning. Early detection of pests is key to crop success, but is typically done manually, which is time consuming and resource intensive. Accurate methods are needed to obtain data quickly and non-invasively. The implemented method consists of analysing images of maize crops using convolutional neural networks(CNN) trained to identify insects. The results show that the developed approach achieves an accuracy of over 95% in detecting the main pests studied, surpassing the accuracy of manual methods. The data generated by computer vision and deep learning techniques can be useful to farmers in making decisions about preventive pest control.