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Plant Disease Classification Using Transfer Learning Methods

Awachat, Nikhil satish (2022) Plant Disease Classification Using Transfer Learning Methods. Masters thesis, Dublin, National College of Ireland.

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The majority of today’s food comprises plant-based diets. Agriculture-based products are easily impacted by a variety of plant diseases. Farmers suffer social, ecological, and financial losses as a result of these infections. It becomes crucial to conduct a thorough and timely analysis of plant diseases. Some illnesses are readily detectable and accessible because they are apparent to human sight. In the past, manual examination of plant diseases was done by specialists in that field. This involves tremendous labor and takes a long time to process. To solve this problem an efficient and effective solution is needed. Processing all types of disease images with absolute accuracy can be simplified extremely thanks to neural networks. This can be implemented using Deep Learning with a combination of some data augmentation techniques. The neural network is helpful in the detection of crop diseases through which the risk factors for the disease can be reduced. In this research work, it is found that using VGG16 with data augmentation on the problem gives remarkable results in classifying plant disease, as the final accuracy of the VGG16 model achieved was 99.67%, and the average precision is 74%, average recall is 75%, and average f1-score is 74%. All the results and evaluations of the various models are compared with the previous research on the same dataset.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Transfer Learning; CNN; VGG16; Crop Diseases
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
S Agriculture > SB Plant culture
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 Jan 2023 17:31
Last Modified: 07 Mar 2023 11:07

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