Machine learning models to detect strawberry plant health from UAVs for real-time applications

The intersection of artificial intelligence (AI), machine learning (ML), and precision agriculture has unlocked unprecedented opportunities for sustainable and efficient farming. Leveraging these technologies allows for analyzing extensive agricultural data to monitor plant health, predict yields, and implement data-driven interventions. This paper presents a real-time, machine learning-based framework developed for assessing the health of strawberry plants using RGB imagery collected via UAVs. Advanced object detection models including YOLOv11 and Faster R-CNN with an SSD MobileNet V3 backbone were trained on labeled RGB images and will be deployed on an Nvidia Jetson Nano for, on-site, instantaneous evaluation. The framework detects individual plants, classifies their health based on visual patterns, and enables edge-based inference without the need for external computational infrastructure. Results revealed that Faster R-CNN achieved a superior F1 score of 81.59%, while YOLOv11-Large (YOLOv11l) reached 56.22%, demonstrating the practical applicability and effectiveness of real-time RGB-based monitoring in precision agriculture.