Web and Mobile Apps for Fruit Disease Detection: A Survey

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Puneet Dixit; Prof Sneha Vanjari; Chaitanya Andhale; Sumit Jaiswar

Web and Mobile Apps for Fruit Disease Detection: A Survey

Authors: Puneet Dixit, Prof Sneha Vanjari, Chaitanya Andhale, Sumit Jaiswar

Unique Paper ID: 179610
Volume: 11
Issue: 12
PageNo: 7355-7360

Keywords: Plant disease detection Artificial intelligence (AI) Machine learning (ML) Image analysis Mobile applications Web applications Smart agriculture Digital agriculture Farmer support tools Plant health monitoring Agricultural technology Usability Digital literacy Detection accuracy Language support Agricultural databases.

Abstract:
With the increasing global reliance on agriculture and the need for timely intervention in managing plant diseases, web and mobile applications have emerged as crucial tools for farmers, especially in fruit cultivation. This paper surveys prominent mobile and web-based platforms that utilize artificial intelligence (AI) and machine learning (ML) to detect plant diseases through image analysis. Focusing on usability for farmers with limited digital literacy, the study evaluates apps based on detection accuracy, user interface simplicity, language support, integration with agricultural databases or extension services, and digital reach. Notable platforms such as Plantix, Tumaini, PlantVillage Nuru, Agrix Tech, MyIPM, and Save Our Citrus are profiled in detail. These apps vary in their scope, technology, and regional focus, but collectively represent a growing trend in AI-powered agricultural support systems. A comparative analysis highlights their strengths and limitations, emphasizing the importance of localization, offline capabilities, and expert integration. This survey aims to inform researchers, developers, and policymakers about the current landscape of digital plant health tools, paving the way for more inclusive and effective solutions in smart agriculture.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 7355-7360

Web and Mobile Apps for Fruit Disease Detection: A Survey

Available:https://ijirt.org/article?manuscript=179610

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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