An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowled...

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Vydáno v:	Multimedia tools and applications Ročník 84; číslo 4; s. 1697 - 1724
Hlavní autoři:	Shukla, Preeti, Chandanan, Amit Kumar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2025 Springer Nature B.V
Témata:	Algorithms Artificial neural networks Cluster analysis Clustering Computer Communication Networks Computer Science Computer vision Crop diseases Data Structures and Information Theory Deep learning Feature extraction Heuristic methods Image filters Machine learning Medical imaging Multimedia Information Systems Neural networks Optimization algorithms Performance measurement Plant diseases Recurrent neural networks Shape optimization Special Purpose and Application-Based Systems Texture Vector quantization Voting Crop disease detection SIFT RNN COA Improved binary gabor pattern Improved coyote optimization algorithm GLCM
ISSN:	1573-7721, 1380-7501, 1573-7721
On-line přístup:	Získat plný text
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