A Survey on Different Plant Diseases Detection Using Machine Learning Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250152" target="_blank" >RIV/61989100:27240/22:10250152 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-9292/11/17/2641" target="_blank" >https://www.mdpi.com/2079-9292/11/17/2641</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/electronics11172641" target="_blank" >10.3390/electronics11172641</a>

Result language

angličtina

Original language name

A Survey on Different Plant Diseases Detection Using Machine Learning Techniques

Original language description

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer&apos;s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Electronics

ISSN

2079-9292

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

17

Country of publishing house

CH - SWITZERLAND

Number of pages

29

Pages from-to

2641-2670

UT code for WoS article

000852567100001

EID of the result in the Scopus database

2-s2.0-85137804038