USE OF COMPUTER VISION FOR INSECT RECOGNITION

Resumo

The use of technologies such as computer vision has been a trend in agriculture. This technology can reduce the effort and time to obtain data about crops, improving production, crop health and bringing more sustainable practices. This article presents a literature review, among the topics covered, the article includes algorithms and computational techniques, including Convolutional Neural Networks (CNNs), YOLO (You Only Look Once) and Support Vector Machines (SVMs). These techniques are used in conjunction with various image processing techniques to detect and classify insects. Machine learning techniques have been shown to be effective in classifying insects based on characteristics such as color, size and position. Different datasets, such as IP102, Pest24, Xie1, Xie2 and Wang, were used to train and evaluate these algorithms. This article concludes the effectiveness of deep learning algorithms, particularly YOLOv5 and Faster R-CNN, in insect detection and classification, suggesting a promising future for automated insect monitoring.