Plant Disease and Pest Identification using Alexnet

Authors

  • Hema M., Saraswathi T., Gopinath L., Komateswaran A., Lakshmi Priya K., Reshmi Jaya Soundari R., Yogeshwari S.

Keywords:

CNN, Deep Learning, Machine learning, IPM, Image recognition, Integrated pest management, Plant heath, Plant diagnostics, Pests

Abstract

The agricultural sector faces increasing challenges from plant diseases and pests, resulting in significant yield losses. Timely identification of both the disease and associated pest is critical for effective control measures. This paper introduces an innovative plant disease and pest identification system using the AlexNet CNN algorithm.Our system streamlines identification by allowing users to upload images of affected plants. The AlexNet CNN model, trained on a diverse dataset, accurately recognizes and classifies visual patterns indicative of specific diseases and pests. The two-stage process first identifies the disease and then predicts the associated pest.Integration of the AlexNet CNN enhances accuracy, overcoming challenges of manual methods. Extensive experiments with diverse datasets demonstrate the system's robustness and high accuracy. The user-friendly interface makes it accessible to farmers and agricultural experts. This research contributes to precision agriculture by providing an automated tool for early detection and management, promising to reduce crop losses and enhance agricultural productivity for sustainable food systems.The suggested method provides a user-friendly interface, ensuring accessibility for individuals with diverse technical backgrounds in agriculture. This proposed system shows potential for substantial reductions in crop losses, ultimately boosting overall agricultural productivity and playing a role in the development of sustainable and resilient food systems.Furthermore, our project not only identifies plant diseases but also pinpoints the specific pests responsible, offering comprehensive insights for targeted pest management. This dual functionality ensures a holistic approach to crop protection, maximizing the effectiveness of control strategies.

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Published

16.03.2024

How to Cite

Komateswaran A., Lakshmi Priya K., Reshmi Jaya Soundari R., Yogeshwari S., H. M. S. T. G. L. (2024). Plant Disease and Pest Identification using Alexnet. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 922–929. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5372

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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