Year: 2025
Type: article
Abstract: : Agricultural productivity together with loss reduc- tion depends heavily on prompt disease detection methods and disease classification procedures. The solution built around the InceptionV3 deep learning model provides identification of crop diseases based on uploaded images. Users register through the platform byverifyingtheir email withOTP thentheycanupload images that trigger disease prediction with precise classifications plus given treatment recommendations. Amultilingualchatbot poweredbyAIrunsthroughPerplexity API for instant communication about crop health and disease protection. The system provides complete data storage capabil- ities for predictions through its database structure so users can produce historical reports to improve their monitoring effec- tiveness and decision-making processes. The platform contains multilingual capabilities which allow diverse linguistic farmersto use its features. The project combines Django with SQLite3 database archi- tecture for building the backend alongside an interface frontend that employs HTML, CSS, JavaScript and Bootstrap program- ming elements. The system functions as a complete answer for agricultural operations where it helps farmers prevent diseasesin advance thus minimizing crop damage and creating better farming efficiency.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4408737004