CarePlantify: Agentic AI for Smart Crop Advisory Using CNN and Gemini Vision
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Abstract
— A majority of small and marginal farmers in India continue to rely on traditional knowledge and local intermediaries for critical decisions related to crop selection, pest management, and fertiliser use. These practices frequently lead to reduced crop yields, excessive input costs, and environmental degradation. This paper presents a Smart Crop Advisory System that integrates Agentic AI and Generative AI (GenAI) with Convolutional Neural Networks (CNN) and Gemini Vision for plant disease detection, soil-test-based fertiliser recommendation, and multilingual conversational support.
The core novelty lies in an agentic decision pipeline where an uploaded leaf image is processed in parallel by a fine-tuned CNN model for high-accuracy classification and Gemini Vision for contextual visual reasoning. The AI agent fuses these outputs to deliver a grounded crop health advisory that balances quantitative precision with qualitative explainability. The system further addresses specific regional needs by providing real-time, location-specific, and voice-enabled advisory in languages including Hindi, Rajasthani, and Punjabi, Experimental evaluation demonstrates that the CNN model achieves a classification accuracy of 98.3% on plant diseases, while the fertiliser recommendation engine attains an accuracy of 94.7% against laboratory-validated prescriptions. The agentic architecture enables autonomous multi-step task execution with a 95.5% success rate and an average response time of 4.1 seconds, significantly reducing farmer decision-making latency.
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