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Adaptive fusion of K-means region growing with optimized deep features for enhanced LSTM-based multi-disease classification of plant leaves.
- Source :
-
Geocarto International . 2023, Vol. 38 Issue 1, p1-38. 38p. - Publication Year :
- 2023
-
Abstract
- The manual process of plant leaves disease detection takes more time to perform. To achieve successful classification results, a flawless feature extraction process is required for a detection model. Aiming at the localization of diseased-plant leaves, this paper performs complex tasks like segmentation, and multi-disease classification of plant leaves using 'improved segmentation, feature extraction, and classification' models. Here, the Adaptive Fusion of K-Means Region Growing (AFKMRG) accomplishes the abnormality segmentation of leaves. The extracted features are subjected to the Enhanced Long short-term memory (LSTM) for performing the multi-disease classification. Here, the segmentation, classification, and feature extraction are improved by the Fitness Sorted Jaya-Forest Optimization Algorithm (FSJ-FOA). From the empirical results, the accuracy and precision of the designed method is attains 98.35% and 98.40% for all kinds of leaves. Results show that the designed multi-disease classification of plant leaves method provides elevated performance with diverse performance metrics. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10106049
- Volume :
- 38
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Geocarto International
- Publication Type :
- Academic Journal
- Accession number :
- 174880018
- Full Text :
- https://doi.org/10.1080/10106049.2023.2178520