Ai-Powered Crop Disease Detection and Solution System to Empower Rural Entrepreneurs With Limited Education.

dc.contributor.authorRahman, Abdur
dc.date.accessioned2026-04-16T06:00:17Z
dc.date.available2026-04-16T06:00:17Z
dc.date.issued2025-08-02
dc.descriptionProject Report
dc.description.abstractThe whole outcomes of my proposal, " AI-POWERED CROP DISEASE DETECTION AND SOLUTION SYSTEM TO EMPOWER RURAL ENTREPRENEURS WITH LIMITED EDUCATION", may be seen here. This essay goes into great detail about how the idea was transformed into a working website. The user dashboard is one element that system users notice. The project's objective was to develop a web application with image classification and GPT OpenAI integration for Fast API, AI-powered crop disease detection and assistance. intends to create a web application that uses picture recognition to classify agricultural diseases. Users will be able to snap a picture, submit it, and use the app to identify illnesses. An GPT OpenAI integration will also be incorporated to respond to user inquiries and offer answers about ailments that have been identified. An alternative is to establish specified wording for illness information and prevention. There is also an online version that requires uploading images before processing. The goal of this research is to create a picture web categorization system for crop disease identification. Users may take pictures of afflicted crops or submit them, and the system will accurately identify the illnesses. Four crops, each with three to four illnesses, will be supported by the system for categorization. An integrated Fast API GPT OpenAI integration will also giveanswers to user questions, solutions, or predetermined data on the specifics of the illness, prevention, and therapy. There will also be an online version that requires image submissions in order to diagnose diseases. This system gives farmers immediate, AI-driven information to improve agricultural decision-making. Every aspect of the system development process, from idea to execution, is covered in the research, including the technologies used, architecture, and user interface design. Python was used for the backend and Frontend use stream lit. All you need is a standard desktop computer and internet access to set up our system application; expensive software or computer components are not required.
dc.identifier.citationCIS
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16840
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16840
dc.language.isoen_US
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectRural Empowerment
dc.subjectCrop Disease Detection
dc.subjectArtificial Intelligence (AI)
dc.subjectPrecision Agriculture
dc.titleAi-Powered Crop Disease Detection and Solution System to Empower Rural Entrepreneurs With Limited Education.
dc.typeWorking Paper

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