GUI-Based End-to-End Deep Learning Model for Corn Leaf Disease Classification

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Author(s): G. Revathy (SASTRA University, India), J. Jeyabharathi (Kalasalingam Academy of Research and Education, India), Madonna Arieth (Sreenivasa Institute of Technology and Management Studies, India)and A. Ramalingam (Sri Manakula Vinayagar Engineering College (Autonomous), India)
Copyright: 2023
Pages: 22
Source title: Machine Learning and Deep Learning for Smart Agriculture and Applications
Source Author(s)/Editor(s): Mohamamd Farukh Hashmi (National Institute of Technology, Warangal, India)and Avinash G. Kesakr (Visvesvaraya National Institute of Technology, India)
DOI: 10.4018/978-1-6684-9975-7.ch010

Keywords: Agricultural and Food Sciences / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Food security is a major problem worldwide. Ensuring that the crops produced are both safe and wholesome is crucial not only for people as the ultimate consumers of the crops, but also for farmers. Plant diseases are responsible for a significant percentage of crop losses. This alleviates the need for a fast and accurate model to discriminate and identify plants with diseases. The base chapter chosen aims to achieve the same through deep learning. The data set used in the work was obtained from Plant Village Dataset. The work customs deuce pre-trained models, EfficientNetB0 and DenseNet121, to citation the traits of the plants. The extracted traits are then fused together through concatenation to allow the model to read the more meaningful crop trait data. This also ensures that the different sets of feature data read by the two models compensate for any feature loss during extraction. It turns out that the above method gives better results associated to other models.

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GUI-Based End-to-End Deep Learning Model for Corn Leaf Disease Classification

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