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| Autores principales: | , |
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| Formato: | Recurso digital |
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| Publicado: |
Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.17999860 |
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- <p>Agriculture in India faces challenges due to climate variability, soil nutrient depletion, and limited access to expert guidance. This paper presents Harvestify (CropCareAI), an AI-powered system that provides crop recommendations, fertilizer suggestions, plant disease detection, weather intelligence, and interactive chatbot support for Indian farmers. The system integrates machine learning models, deep learning CNN-based disease detection, and real-time weather data, with multi-language support for English, Hindi, Tamil, Marathi, and Kannada. Field deployment demonstrates increased crop yield, optimized fertilizer usage, and early disease management, making agriculture more efficient and data-driven.</p> <p>The platform is designed with a responsive web interface for mobile and desktop, ensuring usability across diverse Indian farming communities. Field testing and simulated experiments demonstrate that Harvestify can increase crop yield by 15–20%, reduce fertilizer waste by 30%, and detect plant diseases early in 40% of monitored cases. By combining machine learning, deep learning, and real-time data in a multi-lingual, user-friendly platform, Harvestify provides a scalable and practical solution for sustainable agriculture. It highlights the potential of AI-driven systems in enhancing productivity, reducing resource wastage, and improving decision-making among farmers in resource-limited regions.</p>