The classification of pests using computer vision and Convolutional Neural Networks (CNNs) involves leveraging deep learning toidentify and categorize pest species from images. By training a CNN model ...

The goal of this paper is to create a sophisticated computer vision model that is especially intended for the automatic identification and categorization of diseases affecting corn plants. The study ...

This paper presents a new methodology that utilizes computer vision techniques for the automatic detection and classification of stages in wildflower growth. Wild plant development monitoring is ...

Symmetry detection represents a cornerstone in computer vision, fundamentally enhancing object recognition, reconstruction, and classification. Its significance lies in the capacity to identify ...

Swin Transformer achieves state-of-the-art results on multiple benchmarks, including object detection and semantic segmentation, proving transformers’ applicability beyond classification.

Classifying pests in agricultural settings using computer vision and Convolutional Neural Networks (CNNs) is a powerful approach to automated pest detection. CNNs are highly effective for image ...