Data Science and Intelligent Computing Techniques
Plant Disease Detection Using CNN
Authors: Silpa Chaitanya P, Harshini K, Moni Priyanka K, Pranavika Sri K and Pavani D
Publishing Date: 10-01-2023
ISBN: 978-81-955020-2-8
Abstract
Plant disease identification is the study based upon the fact of patterns. Plant diseases make production more difficult to produce. To avoid losses in agricultural productivity and quantity, disease detection is used. Deep learning can help to avoid the problems of manually choosing disease spot features, raise the accuracy of plant disease extracting features, and increase speed scientific and innovation transition. The combination of increased smartphone usage and deep learning-enabled advancements in computer vision has paved the path for smartphone-assisted virus diagnosis. We used images to train the data and perform the CNN algorithm. One of the key concerns that determines the deficit of harvest production and agricultural production is the detection and discovery of plant diseases. Plant infection investigations are the examination of any visible focus in any part of the plant that aids us in distinguishing any two plants, in particular any spots or shading conceals. One of the most important factors in the horticultural turn of events is the plant's ability to be maintained. It's exceedingly difficult to get the appropriate identifiable proof of plant illnesses. The recognizable proof of illness needs considerable effort and experience, as well as extensive understanding of plants and the research into the diseases' identification.
Keywords
Deep Learning, Plant Disease, Segmentation, Classification, Feature Extraction, Image Processing, Convolutional Neural Network.
Cite as
Silpa Chaitanya P, Harshini K, Moni Priyanka K, Pranavika Sri K and Pavani D, "Plant Disease Detection Using CNN", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 47-54. https://doi.org/10.56155/978-81-955020-2-8-5