PEST DETECTION USING DEEP CONVOLUTIONAL NUERAL NETWORK

ABSTRACT The Pest Detection System utilizing Deep Convolutional Neural Networks (CNNs) is designed to accurately identify and classify objects within images through advanced machine learning techniques. By leveraging the hierarchical feature extraction capabilities of CNNs, the system processes visual data to recognize patterns, shapes, and textures, enabling it to distinguish between different categories of objects effectively. This project implements a robust architecture that includes convolutional layers, pooling layers, and fully connected layers, allowing for high accuracy in image classification tasks. The system was trained on a diverse dataset, utilizing techniques such as data augmentation and transfer learning to enhance performance and generalization. Rigorous testing demonstrated the model's ability to achieve high accuracy rates across various image recognition tasks. The findings highlight the potential of deep learning in transforming image analysis and recognition processes, paving the way for future advancements and applications.