YOLOv11n for precision agriculture: lightweight and efficient detection of guava defects across diverse conditions

Automated fruit defect detection plays a critical role in improving postharvest quality assessment and supporting decision-making in agricultural supply chains. Guava defect detection presents specific challenges because of diverse disease types, varying maturity levels and inconsistent environmental conditions. Although existing you only look once (YOLO)-based models have shown promise in agricultural detection tasks, they often face limitations in balancing detection accuracy, inference speed and computational efficiency, particularly in resource-constrained settings. This study addresses this gap by evaluating four YOLO models (YOLOv8s, YOLOv5s, YOLOv9s and YOLOv11n) for detecting defective guava fruits across five diseases (scab, canker, chilling injury, mechanical damage and rot), three maturity levels (mature, half-mature and immature) and healthy fruits.

Diverse datasets facilitated robust training and evaluation. YOLOv11n achieved the highest mAP50-95 (98.0%) and exhibited bounding box loss (0.0565), classification loss (0.2787), inference time (3.9 milliseconds) and detection speed (255 FPS). YOLOv5s had the highest precision (94.9%), while YOLOv9s excelled in recall (96.2%). YOLOv8s offered a balanced performance across metrics. YOLOv11n outperformed all models with a lightweight architecture (2.6 million parameters) and low computational cost (6.3 giga floating-point operations per second), making it suitable for resource-constrained applications.

These results highlight YOLOv11n's potential for agricultural applications, such as automated defect detection and quality control, which require high accuracy and real-time performance across diverse conditions. This analysis provides insights into deploying YOLO models for agricultural quality assessment to enhance the efficiency and reliability of postharvest management. © 2025 Society of Chemical Industry.