• Dr.vandana singh
• Vinita Thakur
• Priya Singh

Plant diseases significantly affect agricultural productivity and food security across the world. Early and accurate detection of plant diseases is essential to minimize crop loss and ensure sustainable farming practices. Traditional plant disease detection systems rely on Convolutional Neural Networks (CNNs), which require large labeled datasets and extensive model training. This paper presents PlantScan, a plant species and disease detection system based on Generative Artificial Intelligence using a Large Multimodal Model (LMM). The proposed system utilizes Llama Vision via the Groq API to analyze plant leaf images and identify diseases in a zero-shot manner, without the need for dataset-specific training. PlantScan references the standard PlantVillage Dataset to define detectable species and disease classes. The system provides disease identification along with natural language explanations and treatment suggestions, making it suitable for real-time agricultural assistance.

Plant Disease Detection, AI, Computer Vision, Zero-Shot Learning, Smart Agriculture

"PLANT SPECIES AND DISEASE DETECTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.d63-d68, January-2026, Available :http://www.jetir.org/papers/JETIR2601308.pdf

"PLANT SPECIES AND DISEASE DETECTION USING AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppd63-d68, January-2026, Available at : http://www.jetir.org/papers/JETIR2601308.pdf