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Sayed Abdulhayan, Ahmed Uvais, Fathimath Afreena, Ifrath Begum, Mariam Reema,
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- Professor,, Student,, Student,, Student,, Student, P A College of Engineering,, P A College of Engineering,, P A College of Engineering,, P A College of Engineering,, P A College of Engineering, Mangalore,, Mangalore,, Mangalore,, Mangalore,, Mangalore, India, India, India, India, India
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Abstract
nMachine learning techniques, including traditional (shallow) ML, deep learning (DL), and augmented learning (AL), are being increasingly utilized for leaf disease classification. These methods involve feature extraction, data augmentation, and transfer learning to enhance model effectiveness and reduce the need for labeled data. The success of machine learning approaches in this domain hinges on the quality and quantity of data available. LeafGuard is a cutting-edge device with intelligent sensing systems for plant health monitoring and maintenance. LeafGuard provides a comprehensive solution for detecting and resolving plant health issues through the use of sophisticated sensors, machine learning algorithms, and real-time data processing. This article explores the features, advantages, and possible uses of LeafGuard in horticulture and agriculture, as well as its operating principles.
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Keywords: Machine learning techniques. deep learning, augmented learning, including traditional (shallow) ML
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Electronics Communication Systems(rtecs)]
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References
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | June 18, 2024 | |
| Accepted | July 20, 2024 | |
| Published | August 14, 2024 |
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