Drone-Based Smart Farming for Precision Detection and Estimation of Pineapple Plants

Precise quantification of pineapple (Ananas comosus) cultivation is vital for strategic agricultural decisionmaking and operational efficiency. Traditional manual enumeration methods are resource-intensive, time-consuming, and susceptible to human error. This research presents an automated detection framework utilizing unmanned aerial vehicle (UAV) photography combined with digital image analysis techniques. A YOLO-formatted dataset of high-resolution photos was gathered in Pujananting, Barru, South Sulawesi, Indonesia. An object detection model built on the YOLOv8 architecture was then trained using these pictures. Standard criteria such as precision, recall, and mean Average Precision at a 0.5 Intersection over Union threshold were used to assess the model. Experimental findings indicate the proposed framework achieves excellent detection performance, with mean Average Precision attaining 0.979 and both precision and recall reaching 0.95. The framework was additionally validated for automated plant enumeration, with outcomes aligning closely with manual field observations. These findings demonstrate that UAV-based imaging integrated with deep learning provides an accurate and efficient methodology for pineapple crop monitoring, supporting intelligent agricultural applications and informed management strategies.