LEAFDOC: AN END-TO-END SYSTEM FOR REAL-TIME PLANT DISEASE DIAGNOSIS AND ADVISORY USING GENERATIVE AI
DOI:
https://doi.org/10.63503/c.acset.2025.9Keywords:
Computer Vision, Plant Pathology, Generative AI, MERN Stack, Transfer Learning, Agricultural Technology, Decision Support SystemsAbstract
Plant diseases significantly impact global agriculture, leading to large-scale yield losses and threatening food security. Existing deep learning models provide diagnostic accuracy but lack integrated remediation guidance. This paper presents LeafDoc, an end-to-end system combining EfficientNet-B0-based classification and a generative AI advisory engine to deliver real-time disease diagnosis and management recommendations. Achieving 98.7% classification accuracy, the system outperforms existing solutions like Plantix and Agrio. Methodology, motivation, system architecture, and comparative evaluation with existing models are presented in detail.
References
1. Mohanty, S.P., et al. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2016. 7: p.1419.
2. Arnal Barbedo, J.G. Plant disease identification from individual lesions. Biosystems Engineering, 2019. 172: p.84–91.
3. Singhal, K., et al. Large language models encode clinical knowledge. Nature, 2023. 618: p.1–10.
4. Shakeel, S. Role of artificial intelligence in plant disease detection: A review. Emerging Research Nexus, 2024. 1: p.1–10.
5. Khan, S. A review on automated plant disease detection. SpringerLink, 2025.
6. Nautiyal, M. Revolutionizing agriculture: A comprehensive review on AI tools. ScienceDirect, 2025.
7. Neik, T.X. Plant disease epidemiology in the age of artificial intelligence. ScienceDirect, 2025.
8. Gupta, G. Applications of AI in precision agriculture. SpringerLink, 2025.
9. Artificial intelligence and its applications in agriculture. ResearchGate, 2025.
10. Artificial intelligence in agriculture: A comprehensive review. J Neonatal Surg, 2025. 14(1): p.1–10.
11. AI-based platforms for real-time farmer decision support. Journal of Engineering Research and Applications, 2025. 5(4): p.1–10.
12. Generative AI for agriculture advisory. CABI, 2025.
13. Explainable AI for cotton leaf disease classification. PMC, 2025.
14. AI-driven plant disease detection with tailored models. Taylor & Francis Online, 2025.
15. MobilePlantViT: A mobile-friendly hybrid ViT for generalized plant disease image classification. arXiv preprint, 2025.
16. FloraSyntropy-Net: Scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis. arXiv preprint, 2025.
17. AgroXAI: Explainable AI-driven crop recommendation system for Agriculture 4.0. arXiv preprint, 2024.
18. Plant disease detection through multimodal large language models and convolutional neural networks. arXiv preprint, 2025.
19. A critical review of farmer-led experiences with the 'Ama Krushi' chatbot. Agricultural Communication and Research International, 2025. 5(2): p.1–10.
20. Artificial intelligence in India. Wikipedia, 2025.
21. Farmers Business Network. Wikipedia, 2025.
22. Future farming in India: A playbook for scaling artificial intelligence. World Economic Forum, 2025.
23. Agriculture and artificial intelligence: 2025 AI farming trends. Farmonaut, 2025.
24. Advancing plant leaf disease detection integrating machine learning and deep learning. Nature, 2024.
25. Past, present and future of deep plant leaf disease detection. ScienceDirect, 2025.
26. Real-time plant disease detection by AI. EJEMBA, 2025.
27. AI-driven plant disease detection with tailored models. Taylor & Francis Online, 2025.
28. AI-based platforms for real-time farmer decision support. Journal of Engineering Research and Applications, 2025. 5(4): p.1–10.
29. Artificial intelligence in India. Wikipedia, 2025.
30. Artificial intelligence in India. Wikipedia, 2025.
31. Artificial intelligence in India. Wikipedia, 2025.
