Developing a Multi-Level Nitrogen Malnutrition Detection on Paddy Leaf Using YOLOv11 - Said Omar, Muhammad Syahmi; Md Taujuddin, Nik Shahidah Afifi; Sari, Suhaila (Evolution in Electrical and Electronic Engineering, 2026)
This project focuses on developing a YOLOv11 deep learning model to classify leaf nitrogen levels to support efficient crop health monitoring in precision agriculture. A dataset of 6,000 annotated images, representing nitrogen levels 2 to 5 based on the Leaf Colour Chart (LCC), was split into training (80%), validation (10%), and testing (10%) sets. The model achieved peak stability and accuracy at Epoch 130. For practical field use, the model was deployed into an Android mobile application via TensorFlow Lite. Real-time testing showed detection accuracy of 86% for Level 5 and 74% for Level 2, while intermediate levels 3 and 4 achieved lower rates of 56% and 46% due to visual color similarities. These findings demonstrate that YOLOv11 is a reliable tool for automated nutrient monitoring.