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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 12, ISSUE 3, MARCH 2023

Leaf Detection System

Ms. Nakshatra Digambar Chaudhari, Mr. Gaurav Hiraman Sonawane, Ms. Roshani Sukdev Nagare, Ms N.S Gite

DOI: 10.17148/IJARCCE.2023.12345

Abstract: The Indian economy relies heavily on agricultural productivity. Hence, both the environment and people greatly depend on the contribution of food and cash crops. Many diseases claim the lives of crops every year. Many plants perish as a result of poor diagnosis of these diseases and lack of awareness of the symptoms and cure. The overview of plant disease detection using various algorithms is provided by this work. Here, a CNN-based technique for identifying plant diseases is suggested. On sample photos, simulation research and analysis are carried out in terms of time complexity and the size of the infected area. It is carried out using image processing methods Cases have been fed to the model, out of which some cases are of diseased plant leaves Test accuracy is obtained as 89.80%.

Keywords: CNN Algorithm, Open CV, Keras, TensorFlow, Image Segmentation

How to Cite:

[1] Ms. Nakshatra Digambar Chaudhari, Mr. Gaurav Hiraman Sonawane, Ms. Roshani Sukdev Nagare, Ms N.S Gite, “Leaf Detection System,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.12345