PEST INSECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK

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V. Arockia Mary Epsy; Dr. P. Radha

PEST INSECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Authors: V. Arockia Mary Epsy, Dr. P. Radha

Unique Paper ID: 168081
Volume: 11
Issue: 4
PageNo: 1297-1304

Keywords: Convolutional Neural Networks Model Generalization Pest Classification Pest Detection Sustainable Agriculture

Abstract:
Pest insect detection is critical for effective crop management and sustainable agriculture. Traditional methods of pest control often rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. This paper explores the application of Convolutional Neural Networks (CNN) for automated pest insect detection. CNNs, with their deep learning architecture, are well-suited for image recognition tasks and offer the potential for high accuracy in identifying and classifying various insect species. By training a CNN on a dataset of pest images, the system can automatically detect and classify pests in real-time, helping to reduce pesticide use and improve crop yield. The proposed model is evaluated on its accuracy, precision, and recall, demonstrating its effectiveness in identifying a range of pest species. The results show that CNN-based pest detection systems can significantly enhance agricultural productivity and pest management practices by offering a scalable, automated solution that minimizes human intervention. However, challenges such as data availability, environmental variations, and model generalization remain key areas for further research.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 4
PageNo: 1297-1304

PEST INSECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Available:https://ijirt.org/article?manuscript=168081

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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