Multi-Head Attention-Based Transfer Learning Approach for Potato Disease Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0185640" target="_blank" >RIV/00216305:26220/26:0185640 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Head Attention-Based Transfer Learning Approach for Potato Disease Detection

Popis výsledku v původním jazyce

Potatoes are widely consumed all over the world. Being one of the most cultivated crops around the world, they also attract various diseases. Hence, the early identification of such diseases using machine learning-based automated methods, is necessary. In this paper, the solution for the early detection of two most commonly occurring diseases in potato leaves, i.e. Early blight and Late blight have been proposed. In this work, a VGG16 model has been fine-tuned with a multihead attention layer for identifying useful patterns for the classification of potato plant leaf diseases. The multi-head attention mechanism is useful since it can capture the relationship that exists in different parts of an input potato disease leaf image. The proposed model has attained an accuracy of 91% with an F1-score of 0.9103. The better performance of the proposed model is a testimony to its effectiveness in the early identification of potato leaf disease.

Název v anglickém jazyce

Multi-Head Attention-Based Transfer Learning Approach for Potato Disease Detection

Popis výsledku anglicky

Potatoes are widely consumed all over the world. Being one of the most cultivated crops around the world, they also attract various diseases. Hence, the early identification of such diseases using machine learning-based automated methods, is necessary. In this paper, the solution for the early detection of two most commonly occurring diseases in potato leaves, i.e. Early blight and Late blight have been proposed. In this work, a VGG16 model has been fine-tuned with a multihead attention layer for identifying useful patterns for the classification of potato plant leaf diseases. The multi-head attention mechanism is useful since it can capture the relationship that exists in different parts of an input potato disease leaf image. The proposed model has attained an accuracy of 91% with an F1-score of 0.9103. The better performance of the proposed model is a testimony to its effectiveness in the early identification of potato leaf disease.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

21101 - Food and beverages

Projekt

<a href="/cs/project/CK04000027" target="_blank" >CK04000027: Systém řízENí Dopravy nové gEneRace (SENDER)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

165-169

Název nakladatele

—

Místo vydání

Gent

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—