Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/358281
Title: | Development of Efficient CNN models for plant disease identification |
Researcher: | Agarwal, Mohit |
Guide(s): | Gupta, Suneet Kr. and Biswas, K. K. |
Keywords: | CNN models Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Bennett University |
Completed Date: | 2021 |
Abstract: | Automatic identification of plant disease from leaf images has been a subject of interest for more than two decades. A number of Machine Learning (ML) algorithms and Convolution Neural Network (CNN) models have been proposed for identification of various crop diseases. CNN models are based on Deep Learning Neural Networks and differ inherently from traditional Machine Learning algorithms like k-NN, Decision-Trees etc. Moreover, the performance of Deep Neural Network based approaches are better as compared to traditional Machine Learning approaches as these models extract the features from training data automatically. In past, the researchers have proposed many CNN architectures such as VGG16, VGG19, InceptionV3, MobileNet, ResNet50, etc. for the classification of 1000 class imagenet dataset. These models can also be utilized for the classification of other data sets by transfer learning. While pre-trained CNN models perform fairly well, they tend to be computationally heavy due to large number of parameters involved. |
Pagination: | |
URI: | http://hdl.handle.net/10603/358281 |
Appears in Departments: | School of Computer Science Engineering and Technology |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 73.29 kB | Adobe PDF | View/Open |
02_table of content.pdf | 68.25 kB | Adobe PDF | View/Open | |
03_declaration.pdf | 108.35 kB | Adobe PDF | View/Open | |
04_certificate.pdf | 95.32 kB | Adobe PDF | View/Open | |
05_acknowledgment.pdf | 655.65 kB | Adobe PDF | View/Open | |
06_abstract.pdf | 50.93 kB | Adobe PDF | View/Open | |
07_list of acronyms.pdf | 49.21 kB | Adobe PDF | View/Open | |
08_list of symbols.pdf | 71.75 kB | Adobe PDF | View/Open | |
09_list of figures.pdf | 56.69 kB | Adobe PDF | View/Open | |
10_list of tables.pdf | 83.67 kB | Adobe PDF | View/Open | |
11_list of algorithms.pdf | 49.06 kB | Adobe PDF | View/Open | |
12_chapter1.pdf | 1.04 MB | Adobe PDF | View/Open | |
13_chapter2.pdf | 1.81 MB | Adobe PDF | View/Open | |
14_chapter3.pdf | 4.75 MB | Adobe PDF | View/Open | |
15_chapter4.pdf | 2.86 MB | Adobe PDF | View/Open | |
16_chapter5.pdf | 3.23 MB | Adobe PDF | View/Open | |
17_chapter6.pdf | 55.96 kB | Adobe PDF | View/Open | |
18_bibliography.pdf | 109.54 kB | Adobe PDF | View/Open | |
19_lisr of publications.pdf | 68.67 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 128.67 kB | Adobe PDF | View/Open |
Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Altmetric Badge: