• MISS.GAYATRI A. RAHANGADALE
• Prof. Ashish Manusmare

Abstract - In agriculture, the early detection of crop diseases and nutritional deficiencies is critical for ensuring food security and crop yield optimization. This paper presents a novel approach using smart image processing and transfer learning in Python to address the specific challenge of rice disease and nutritional deficiency detection. The system employs a Python-based image processing framework, supplemented by a diverse dataset of rice plant images, including healthy plants, plants afflicted with various diseases, and plants with nutritional deficiencies. MobileNetV2, combined with transfer learning techniques, forms the core of the paper, enabling accurate and efficient classification of rice plant health. The system is designed with a user-friendly graphical user interface (GUI), making it accessible to a wider audience. This paper offers a promising solution to support farmers in identifying and mitigating crop health issues in a timely manner, thereby contributing to agricultural sustainability.

Key Words: Smart agriculture, Image classification, Rice Disease Detection, Nutritional Deficiency Detection.

"RESEARCH ON APPLICATION OF SMART IMAGE PROCESSING AND TRANSFER LEARNING FOR RICE DISEASE AND NUTRITIONAL DEFICIENCY DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.e240-e247, May-2024, Available :http://www.jetir.org/papers/JETIR2405429.pdf

"RESEARCH ON APPLICATION OF SMART IMAGE PROCESSING AND TRANSFER LEARNING FOR RICE DISEASE AND NUTRITIONAL DEFICIENCY DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppe240-e247, May-2024, Available at : http://www.jetir.org/papers/JETIR2405429.pdf