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Identification of Foliar Disease in Apple Trees using Deep Learning Techniques

-, Rajat (2022) Identification of Foliar Disease in Apple Trees using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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The earlier apple foliar disease is identified, the more productive and profitable the crop will be. It is a challenge to correctly label a disease in a foliar plant at the right moment in order to prevent significant losses without the help of technology. Recently, a number of deep learning and machine learning methods, including CNN, VGG-16, ResNet50, and SVM, have been developed to identify apple diseases. In this study, multiple transfer learning algorithms such as LittleVGG, MobileNet, and EfficientNet with a Generative Adversarial Network (GAN) are proposed to identify foliar diseases such as apple scab, apple rust, or multiple diseases on a given leaf image. This method avoids the need for time-consuming and expensive manual scouting activities by humans and enables farmers to take appropriate action in response to the results. According to our research, the EfficientNet model outperformed LittleVGG and MobileNet at detecting apple foliar diseases.

Item Type: Thesis (Masters)
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: 01 Mar 2023 12:31
Last Modified: 01 Mar 2023 17:30

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