Crop Pest Detection using Convolutional Neural Network
Year: 2024
Type: article
Abstract: Pests in plants can cause significant losses in agricultural production. As a result, various technologies are used nowadays to improve agriculture's efficiency and make it more sustainable. This research highlights the contribution of machine learning algorithms and image recognition technologies for pest identification. Farmers can use the system to recognize pests and take the necessary actions to reduce them. Convolutional Neural Networks (CNN) is used in this study for image recognition tasks, including pest identification in agricultural fields. The algorithm is trained using the Agricultural Pests Dataset acquired from Kaggle. The experiment results showed that the CNN performed better than the other state-of-the-art machine learning models, with a much lower false rejection rate of 0.12% and an accuracy of 99%.
Authors: Devika T, Santhiyakumari N, Nagaraj J, Arun S K, Sam Sundhar T, Siva Sakthi K
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4403156542