Development of disease detection mobile application for pineapple

Pineapple (Ananas comosus) is a tropical fruit that is widely distributed throughout the world. The cultivation of pineapples, which holds great economic importance in the field of agriculture, encounters ongoing difficulties regarding the detection and control of diseases that affect both the quant...

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Published in:IOP Conference Series: Earth and Environmental Science
Main Author: Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
Format: Conference paper
Language:English
Published: Institute of Physics 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206441743&doi=10.1088%2f1755-1315%2f1397%2f1%2f012014&partnerID=40&md5=5a63db0749c4255f069e6f38759e52d2
id 2-s2.0-85206441743
spelling 2-s2.0-85206441743
Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
Development of disease detection mobile application for pineapple
2024
IOP Conference Series: Earth and Environmental Science
1397
1
10.1088/1755-1315/1397/1/012014
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206441743&doi=10.1088%2f1755-1315%2f1397%2f1%2f012014&partnerID=40&md5=5a63db0749c4255f069e6f38759e52d2
Pineapple (Ananas comosus) is a tropical fruit that is widely distributed throughout the world. The cultivation of pineapples, which holds great economic importance in the field of agriculture, encounters ongoing difficulties regarding the detection and control of diseases that affect both the quantity and quality of the yield. Manual disease detection in pineapple crops is challenging due to subjective evaluations, laborious processes, and financial losses. Manual methods can lead to delayed disease identification and specialised knowledge, limiting precise disease diagnosis. The need for effective disease detection tools has prompted the investigation of mobile applications as an ideal solution. To address these issues, automated disease scanning technology systems utilising computer vision and machine learning are essential. The Successive Approximation Model (SAM) was used for instructional design that emphasises a rapid and iterative development process. The development process involves three phases: design, development, and evaluation. The developed mobile application was then evaluated using a questionnaire to get user's feedback and evaluate the accuracy of information provided by the application. Reporting on the result from Likert Scale, most of the score showed values four and above which indicated positive response on the usability and accuracy of disease detection by the mobile application. As a conclusion, the development of this mobile application gives a positive response to users to promptly detect and control diseases, thereby reducing the possibility of crop damage. © 2024 Institute of Physics Publishing. All rights reserved.
Institute of Physics
17551307
English
Conference paper
All Open Access; Gold Open Access
author Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
spellingShingle Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
Development of disease detection mobile application for pineapple
author_facet Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
author_sort Sophan P.N.A.S.; Ismail S.A.; Sadikan S.F.N.
title Development of disease detection mobile application for pineapple
title_short Development of disease detection mobile application for pineapple
title_full Development of disease detection mobile application for pineapple
title_fullStr Development of disease detection mobile application for pineapple
title_full_unstemmed Development of disease detection mobile application for pineapple
title_sort Development of disease detection mobile application for pineapple
publishDate 2024
container_title IOP Conference Series: Earth and Environmental Science
container_volume 1397
container_issue 1
doi_str_mv 10.1088/1755-1315/1397/1/012014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206441743&doi=10.1088%2f1755-1315%2f1397%2f1%2f012014&partnerID=40&md5=5a63db0749c4255f069e6f38759e52d2
description Pineapple (Ananas comosus) is a tropical fruit that is widely distributed throughout the world. The cultivation of pineapples, which holds great economic importance in the field of agriculture, encounters ongoing difficulties regarding the detection and control of diseases that affect both the quantity and quality of the yield. Manual disease detection in pineapple crops is challenging due to subjective evaluations, laborious processes, and financial losses. Manual methods can lead to delayed disease identification and specialised knowledge, limiting precise disease diagnosis. The need for effective disease detection tools has prompted the investigation of mobile applications as an ideal solution. To address these issues, automated disease scanning technology systems utilising computer vision and machine learning are essential. The Successive Approximation Model (SAM) was used for instructional design that emphasises a rapid and iterative development process. The development process involves three phases: design, development, and evaluation. The developed mobile application was then evaluated using a questionnaire to get user's feedback and evaluate the accuracy of information provided by the application. Reporting on the result from Likert Scale, most of the score showed values four and above which indicated positive response on the usability and accuracy of disease detection by the mobile application. As a conclusion, the development of this mobile application gives a positive response to users to promptly detect and control diseases, thereby reducing the possibility of crop damage. © 2024 Institute of Physics Publishing. All rights reserved.
publisher Institute of Physics
issn 17551307
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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