A Lightweight and Efficient Detection Transformer for Early Stage Pomegranate Fruit Detection

The detection of early-stage pomegranate fruits is a crucial task in agriculture for monitoring crop health and optimizing harvesting processes. This project presents a lightweight and efficient detection transformer for early-stage pomegranate fruit detection, utilizing the YOLOv9 model. The system is designed to identify and classify various stages of pomegranate growth, including 'bud,' 'early-fruit,' 'flower,' 'mid-growth,' and 'ripe,' with high accuracy. The model achieves a balanced performance across all classes, with precision, recall, and mAP values demonstrating its robustness in detecting different growth stages. The project leverages advanced deep learning techniques to capture intricate patterns in pomegranate fruit images, ensuring reliable real-time detection and classification. The YOLOv9 model, known for its speed and efficiency, enables fast and accurate predictions suitable for deployment in agricultural monitoring systems. The results show promising outcomes, with mAP50 and mAP50-95 values consistently exceeding 0.7, highlighting the model's effectiveness in early-stage fruit detection. This project paves the way for automated agricultural systems, improving crop management and providing valuable insights for farmers. Keywords: Pomegranate Detection, YOLOv9, Deep Learning, Object Detection, Fruit Classification, Agricultural Monitoring, Early-Stage Detection, Machine Learning, Precision, Recall, mAP.