Advanced Technologies For Plant Disease Detection Leveraging Machine Learning And Artificial Intelligence - Info and Reading Options
By Mayur Kalubhai Tundiya
"Advanced Technologies For Plant Disease Detection Leveraging Machine Learning And Artificial Intelligence" and the language of the book is English.
“Advanced Technologies For Plant Disease Detection Leveraging Machine Learning And Artificial Intelligence” Metadata:
- Title: ➤ Advanced Technologies For Plant Disease Detection Leveraging Machine Learning And Artificial Intelligence
- Author: Mayur Kalubhai Tundiya
- Language: English
“Advanced Technologies For Plant Disease Detection Leveraging Machine Learning And Artificial Intelligence” Subjects and Themes:
- Subjects: ➤ Plant disease detection - Machine Learning - Artificial Intelligence - IoT in agriculture - spectroscopic analysis - wireless communication - Quantum-Dot Cellular Automata - plant extract analysis
Edition Identifiers:
- Internet Archive ID: ➤ httpswww.ijtsrd.comcomputer-scienceartificial-intelligence73773advanced-technolo
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The Internet Archive:
<table border="0" cellpadding="0" cellspacing="0" width="255"><colgroup><col width="255" /></colgroup><tbody><tr> <td height="20" class="xl65" width="255" style="height:15pt;width:191pt;">Plants are essential to the global food supply, yet they are increasingly vulnerable to diseases that cause significant production losses. Traditional plant disease detection methods are labor intensive, error prone, and insufficient for early stage diagnosis. Recent advancements in Machine Learning ML , Deep Learning DL , Internet of Things IoT , wireless communication, and Quantum Dot Cellular Automata QCA nanotechnology provide innovative solutions for precise and efficient disease detection. This study introduces a novel ML and Artificial Intelligence AI based plant extract analyzing device designed to address the challenges of Indian agriculture, such as resource limitations and diverse crop varieties. The device integrates spectroscopic analysis, biochemical data interpretation, and QCA nanotechnology with cloud enabled IoT platforms for real time disease diagnosis. Field trials demonstrated high accuracy 97 in identifying fungal, bacterial, and nutrient related diseases, with superior cost efficiency 85 and reduced data requirements 8 GB . These results underscore its potential to revolutionize agricultural practices by improving early disease detection, minimizing crop losses, and promoting sustainable farming. The findings provide insights into adopting advanced technologies in agriculture and their broader implications for food security and precision agriculture. Mayur Kalubhai Tundiya "Advanced Technologies for Plant Disease Detection: Leveraging Machine Learning and Artificial Intelligence" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-6 , December 2024, URL: https://www.ijtsrd.com/papers/ijtsrd73773.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/73773/advanced-technologies-for-plant-disease-detection-leveraging-machine-learning-and-artificial-intelligence/mayur-kalubhai-tundiya</td> </tr></tbody></table>
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