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@article{179610,
        author = {Puneet Dixit and Prof Sneha Vanjari and Chaitanya Andhale and Sumit Jaiswar},
        title = {Web and Mobile Apps for Fruit Disease Detection: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7355-7360},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179610},
        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.},
        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.},
        month = {May},
        }