G Ravi Kumar

International Institute of Information Technology

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M. Vanitha

Saveetha University

Palthiya Anantha Rao

Koneru Lakshmaiah Education Foundation

Dudekula Mohammed Kaif

Siddhartha Medical College

N. Rajesh

Siddhartha Medical College

Thalakola Syamsundararao

KPR Institute of Engineering and Technology

Dasari Koteswara Rao

Guntur Medical College

B Silambarasan

Siddhartha Medical College

Badugu Samatha

Koneru Lakshmaiah Education Foundation

Salman Ali Suhail

University of Calgary

Kuruma Purnima

Siddhartha Medical College

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Siddhartha Medical College

Koneru Lakshmaiah Education Foundation

Vellore Institute of Technology University

Guntur Medical College

Andhra University

Saveetha University

Velammal Medical College Hospital and Research Institute

Alagappa University

SASTRA University

Indian Space Research Organisation

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Detection of Parkinson's Disease using Machine Learning with Feature Analysis from Audio Signals

Kuruma Purnima M.A. Manivasagam Dudekula Mohammed Kaif M. Vanitha N. Rajesh

Feature (linguistics)

10.1109/icmnwc63764.2024.10872307

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IoT-Enabled Potato Diseases Prediction using Deep Learning

Badugu Samatha G Ravi Kumar Palthiya Anantha Rao Dasari Koteswara Rao Thalakola Syamsundararao

Inspection methods that are both quick and non-destructive, such as those enabled by deep learning and image processing, could be extremely beneficial for monitoring the quality of potatoes and other agricultural products. This research work proposes a Deep Neural Network (DNN) model to visually classify the diseased potatoes. A set of potato images are given as input to the DNN model during the training and testing phase. Further, a Modified Support Vector Machine (MSVM) and a Convolutional Neural Network (CNN) models are used to perform segmentation and feature extraction on the potato images. The proposed model was tested using new modern versions of standard datasets maintained by Canada and the United States agricultural departments. The results of this experiment demonstrated that deep learning model has outperformed both traditional approaches and the Machine Learning models. Accuracy rates of over 99% have been reported for some types of images. While some other types of images have reported 97% accuracy.

Feature Engineering

10.1109/icidca56705.2023.10099611

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Citations (3)