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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2023-12-04
DOI: https://doi.org/10.56294/dm2023135

Disease Detection using Region-Based Convolutional Neural Network and ResNet

By
V. Sushma Sri ,
V. Sushma Sri

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

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V. Hima Sailu ,
V. Hima Sailu

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

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U. Pradeepthi ,
U. Pradeepthi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

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P. Manogyna Sai ,
P. Manogyna Sai

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

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Dr. M. Kavitha ,
Dr. M. Kavitha

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India

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Abstract

In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including Convolutional Neural Networks(CNN), Resnet, ReLu enabling the early detection of infections. The deep learning models leverage the captured images to identify and classify infections at their initial stages. The usage of R-CNN and ResNet technology in agriculture field has brought the tremendous change when we detect the disease in earlier stage of crop. Thus the farmer can take the pest preventive measures in the beginning stage to avoid crop failure.

Keywords:

Resolution,
Drones,
Deep Learning,
ResNet,
Regional Convolutional Neural Network,

How to Cite

1.
Sushma Sri V, Hima Sailu V, Pradeepthi U, Manogyna Sai P, Kavitha DM. Disease Detection using Region-Based Convolutional Neural Network and ResNet. Data and Metadata [Internet]. 2023 Dec. 4 [cited 2024 Jun. 30];2:135. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/135

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 2 (2023)
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