A Novel Approach for Predicting Disease in Plant Using Hybrid Deep Convolutional Neural Network

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A Novel Approach for Predicting Disease in Plant Using Hybrid Deep Convolutional Neural Network

July 5, 2021 Science and Technology 0

Agriculture is India’s most important economic industry. The detection of plant diseases is a key concern in the agriculture industry. An accurate and speedier identification of plant diseases, resulting in significant reductions in economic losses. Manually monitoring plant leaf disease is a highly important task that takes a long time. As a result, an automated solution is required for plant disease detection. Deep learning is quickly becoming the industry standard for picture classification. Researchers have been able to improve the accuracy of object detection thanks to recent advances in Deep Neural Networks. Researchers have already built a few architectures for effective plant disease classification, including the Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD). The well-known Deep Convolutional Neural Network architectures for generic Image categorization are Alex Net, Google Net, and VGG-16. When many diseases afflict the same leaf, however, this architecture does not perform well.

To address this, this study proposes a Hybrid Deep Convolutional Neural Network architecture with segmentation, which consists of five convolutional layers, five pooling layers, and two fully connected layers. Deep convolutional neural networks are extensively employed to assess visual imagery and are frequently utilised in image categorization behind the scenes. The input photos are fed to the CNN in the proposed Faster-RCNN system, increasing the accuracy of images to forecast illnesses from plant leaves.

Author (s) Details

R. Jayavadivel
Department of Computer Science and Engineering, Lovely Professional University, Jalandhar-Delhi, G. T. Road, Phagwara, Punjab, India.

V. Chandrasekar
Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana State, India.

V. Shanmugavalli
Department of Computer Science and Engineering, Vivekanandha College of Engineering for Women, India.

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