| Type of Material: | Thesis |
| Title: | Development of efficient cnn models for plant disease identification |
| Researcher: | Agarwal, Mohit |
| Guide: | Gupta, Suneet Kr. | Biswas, K. K. |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2021 |
| Language: | English |
| Subject: | CNN models | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2021 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-12 17:57:05 | |
| 008 | __ | 261012t2021||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464115 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aAgarwal, Mohit |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aDevelopment of efficient cnn models for plant disease identification |
| 260 | __ | |aGreater Noida|c2021|bBennett University |
| 300 | __ | |dDVD |
| 502 | __ | |d2021|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2021|oDate of Award |
| 518 | __ | |d2017|oDate of Registration |
| 518 | __ | |d2021|oDate of Completion |
| 520 | __ | |aAutomatic 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 |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aCNN models |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGupta, Suneet Kr.|eGuide |
| 700 | __ | |aBiswas, K. K.|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/358281|yShodhganga |
| 905 | __ | |afromsg |
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