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Xplant: An Explainable Plant Disease...

Venkatesh, Madan,

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Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms // Madan Venkatesh.
Author:	Venkatesh, Madan,
Description:	1 electronic resource (77 pages)
Notes:	Source: Masters Abstracts International, Volume: 87-05.
Contained By:	Masters Abstracts International87-05.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31999651
ISBN:	9798297989061

Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms /
Venkatesh, Madan,

Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms /Madan Venkatesh. - 1 electronic resource (77 pages)

Source: Masters Abstracts International, Volume: 87-05.

In today's rapidly evolving world, agricultural productivity has become a global challenge. Despite the rise of modern technologies, interest in agriculture has declined, primarily due to productivity issues caused by the spread of diseases and the difficulty farmers face in accurately diagnosing them. Traditionally, precise disease detection has relied on the expertise of seasoned professionals. To address this, the proposed application, Agriculture AI, empowers farmers by providing accurate disease predictions and actionable insights. The system leverages Ensemble Learning techniques in machine learning (ML) to predict plant diseases effectively. To enhance user trust and transparency, Explainable AI (XAI) is integrated to clarify predictions, making the system more reliable and user-friendly. All data, including predictions and supporting information, are stored in a centralized database. This database is a resource for researchers and developers to evaluate the application's accuracy, identify trends, and notify farmers of new or unidentified diseases. Additionally, the application facilitates communication between farmers, researchers, and developers, allowing users to raise queries and provide feedback. This comprehensive tool connects the gap between AI technology and agricultural needs, supporting global efforts to enhance productivity and sustainability in farming.

English

ISBN: 9798297989061Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep learning algorithms

Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms /
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520 $a In today's rapidly evolving world, agricultural productivity has become a global challenge. Despite the rise of modern technologies, interest in agriculture has declined, primarily due to productivity issues caused by the spread of diseases and the difficulty farmers face in accurately diagnosing them. Traditionally, precise disease detection has relied on the expertise of seasoned professionals. To address this, the proposed application, Agriculture AI, empowers farmers by providing accurate disease predictions and actionable insights. The system leverages Ensemble Learning techniques in machine learning (ML) to predict plant diseases effectively. To enhance user trust and transparency, Explainable AI (XAI) is integrated to clarify predictions, making the system more reliable and user-friendly. All data, including predictions and supporting information, are stored in a centralized database. This database is a resource for researchers and developers to evaluate the application's accuracy, identify trends, and notify farmers of new or unidentified diseases. Additionally, the application facilitates communication between farmers, researchers, and developers, allowing users to raise queries and provide feedback. This comprehensive tool connects the gap between AI technology and agricultural needs, supporting global efforts to enhance productivity and sustainability in farming.
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650 4 $a Agricultural engineering. $3 3168406
653 $a Deep learning algorithms
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653 $a Ensemble Learning techniques
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