An Innovative Method: Identifying Pests Through Artificial Neural Networks and Image Processing

Abstract

Effective and precise pest detection technologies are of the utmost importance on a global scale in order to reduce the negative effects of pests on crop yields. “This research presents a new method for detecting pests using ANNs and image processing tools. By combining machine learning with image analysis”, our suggested approach offers a powerful tool for pest identification in a variety of agricultural contexts. Artificial neural networks (ANNs) make it possible to train specialised models that can visually distinguish between different kinds of pests in digital photos. Research using large real-world datasets has shown that preprocessing techniques improve model performance and feature extraction, making them more efficient and accurate than traditional pest detection methods. This research contributes to the field of precision agriculture by providing a trustworthy and automated method for early pest detection, which allows for prompt action and reduces crop loss. Our method takes use of ANNs—which can learn complex patterns from picture data—by combining the most recent developments in deep learning with image processing. Morphological operations and histogram equalisation are two preprocessing methods that help minimise noise and improve the discriminative power of retrieved features. Our technique has been rigorously tested across multiple datasets with different pest species and habitats. It has proven to be quite accurate and scalable in agricultural settings. “The automation and efficiency benefits of our technology are further highlighted when compared with traditional pest identification methods, such as chemical-based procedures and human inspection”. This research highlights the significant impact that AI and image processing may have on pest control tactics. It paves the door for agricultural systems that are more robust and sustainable, and can better handle new threats as they arise.