東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Xplant: An Explainable Plant Disease...

Venkatesh, Madan,

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms // Madan Venkatesh.
作者:	Venkatesh, Madan,
面頁冊數:	1 electronic resource (77 pages)
附註:	Source: Masters Abstracts International, Volume: 87-05.
Contained By:	Masters Abstracts International87-05.
標題:	Computer science. -
電子資源:	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 /
LDR:02780nmm a22004093i 4500 001 2438114
005 20251130221837.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798297989061
035 $a (MiAaPQD)AAI31999651
035 $a AAI31999651
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Venkatesh, Madan, $e author. $3 3829317
245 1 0 $a Xplant: An Explainable Plant Disease Detection Tool Using Multi-Modal Deep Learning Algorithms / $c Madan Venkatesh.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (77 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 87-05.
500 $a Advisors: Badruddoja, Syed Committee members: Tran, Bang.
502 $b M.S. $c California State University, Sacramento $d 2025.
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.
546 $a English
590 $a School code: 6453
650 4 $a Computer science. $3 523869
650 4 $a Agricultural engineering. $3 3168406
653 $a Deep learning algorithms
653 $a Plant disease detection tool
653 $a Ensemble Learning techniques
690 $a 0800
690 $a 0984
690 $a 0539
710 2 $a California State University, Sacramento. $b Computer Science Department. $e degree granting institution. $3 3818498
720 1 $a Badruddoja, Syed $e degree supervisor.
773 0 $t Masters Abstracts International $g 87-05.
790 $a 6453
791 $a M.S.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31999651