Rice Leaf Nutrient Deficiency Detection System
S.Babu
Associate Professor
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
babudharahas@gmail.com
Vaishnavi Gurram
UG Student
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
Vaishnavigurram07@gmail.com
Ravinder Mogili
Professor and Head
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
mogili.ravinder@jits.ac.in
Mohammed Abdul Sajeed
UG Student
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
abdulsajeed113@gmail.com
Rashmitha Gaddam
UG Student
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
rashmithag82@gmail.com
Mohammed Shafiq Ahmed
UG Student
Dept. of Computer Science and Engineering
Jyothishmathi Institute of Technology and Science
(JNTUH)
Karimnagar, Telanagana, India
shafiqahmed6363@gmail.com
Abstract—Nutrient deficiencies in rice plants, especially nitrogen (N), phosphorus (P), and potassium (K), can cause significant effects on rice yield and quality. Conventional methods for diagnosing nutrient deficiencies in rice leaves, including visual observation and soil analysis, are time-consuming and often unreliable. This research aims to develop an online rice leaf nutrient deficiency diagnosis system using machine learning and image processing techniques. The proposed system uses a DenseNet121 convolutional neural network (CNN) model that is fine-tuned on rice leaf images to achieve high accuracy in diagnosing nutrient deficiencies. The system is built using Flask as the backend framework, SQLite as the database management system, and role-based access control for users (farmers, agronomists, and administrators). The proposed system has been shown to achieve an average accuracy of 94-98% across datasets, providing a scalable and user-friendly solution for precision agriculture
