Authors :
Nitika Arya; Ankit Sharma; Amit Vajpayee
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/9rywdwmp
Scribd :
https://tinyurl.com/yhwu29es
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR885
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Detection and control of plant diseases is
critical to maintaining global food security. Recent
advances in deep learning and computer vision have
revolutionized precision agriculture, especially in
automatic detection of crop diseases. This research aims
to further advance this new trend using deep learning
techniques. It focuses specifically on the use of
convolutional neural networks (CNN), specifically the
VGG19 architecture, for the accurate and efficient
detection of agricultural diseases. The study utilized a
large database containing numerous photographs of
healthy and diseased plants. Adding this information
increases the power and capabilities of the model. The
VGG19 architecture is based on algorithms that use
transfer learning techniques to extract complex
information from images.
Keywords :
Agriculture, Detection, Rice Disease, Iot Architecture System.
Detection and control of plant diseases is
critical to maintaining global food security. Recent
advances in deep learning and computer vision have
revolutionized precision agriculture, especially in
automatic detection of crop diseases. This research aims
to further advance this new trend using deep learning
techniques. It focuses specifically on the use of
convolutional neural networks (CNN), specifically the
VGG19 architecture, for the accurate and efficient
detection of agricultural diseases. The study utilized a
large database containing numerous photographs of
healthy and diseased plants. Adding this information
increases the power and capabilities of the model. The
VGG19 architecture is based on algorithms that use
transfer learning techniques to extract complex
information from images.
Keywords :
Agriculture, Detection, Rice Disease, Iot Architecture System.
