Disease Detection using Region-Based Convolutional Neural Network and ResNet

V. Sushma Sri, V. Hima Sailu, U. Pradeepthi, P. Manogyna Sai and M. Kavitha

Data and Metadata, 2023, vol. 2, 135

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

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:2:y:2023:i::p:135:id:1056294dm2023135

DOI: 10.56294/dm2023135

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