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Apple Leaf Disease Identification and Segmentation Using Enhanced Learning-Driven Feature Representation Model
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Author(s): Harsha Raju (Reva University, India), Veena Kalludi Narasimhaiah (Reva University, India)and Mukil Alagirisamy (Asia Pacific University, Malaysia)
Copyright: 2024
Pages: 24
Source title:
AI and Blockchain Applications in Industrial Robotics
Source Author(s)/Editor(s): Rajashekhar C. Biradar (Reva University, India), Geetha D. (Reva University, India), Nikhath Tabassum (Reva University, India), Nayana Hegde (Reva University, India)and Mihai Lazarescu (Politecnico di Torino, Italy)
DOI: 10.4018/979-8-3693-0659-8.ch013
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Abstract
The field of automatic segmentation and identification of plant diseases from leaf images has become increasingly significant in recent years. There are a number of fungal diseases that affect apple quality and yield, including apple scab, cedar rust, and black rot. In order to prevent crop losses, it is essential to identify these diseases quickly as possible. Despite many approaches being discussed, segmenting the diseased part of leaves with high accuracy and low false positive rates remains a challenging task. This study suggests that by isolating the color background and highlighting the area of interest, a substantial feature set can be constructed to enhance deep learning generalization capability for disease classification. An algorithm for fitness function is developed to represent the features that are relevant to disease classes and optimally adjusts the weights and biases in the training phase. Based on visual outcome and comparative analysis in terms of precision, recall rate, and F1-scores, the efficacy of the proposed work is justified.
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