Enhancing Plant Disease Detection with CNNs and LLMs: A Comprehensive Approach to Diagnosis and Mitigation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0190076" target="_blank" >RIV/00216305:26220/26:0190076 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Enhancing Plant Disease Detection with CNNs and LLMs: A Comprehensive Approach to Diagnosis and Mitigation
Original language description
This study presents a novel approach to plant disease detection by integrating a Convolutional Neural Network (CNN) with an open-source Language Model (LLM) within a user-friendly web application. From a custom-built dataset assembled using open access sources, consisting of 48 classes representing various plant diseases and healthy specimens, the CNN model achieves an impressive accuracy of 99.73% on the test set. The framework employs a robust experimental setup, including meticulous data partitioning and hyperparameter tuning, to ensure effective model training and evaluation. While CNN demonstrates exceptional performance in detecting well-represented diseases, challenges in accurately classifying underrepresented classes are identified, emphasizing the need for data augmentation strategies to enhance model robustness. The integrated LLM enhances user interaction by providing real-time insights and actionable recommendations based on CNN predictions, making the tool accessible to users with varying agricultural expertise. Further work aims to refine the system through dataset expansion and advanced training techniques, ultimately positioning this tool as an asset for sustainable agricultural practices.
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ICUMT 2024; 16th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
ISBN
978-3-8007-6544-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
13-18
Publisher name
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Place of publication
Meloneras, Gran Canaria, Spain
Event location
Meloneras, Gran Canaria, Spain
Event date
Nov 26, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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