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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

6

Strana od-do

13-18

Název nakladatele

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Místo vydání

Meloneras, Gran Canaria, Spain

Místo konání akce

Meloneras, Gran Canaria, Spain

Datum konání akce

26. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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