This study applies YOLOv7-tiny object detection to inspect guava covering and count their quantity. Real-time monitoring enhances efficiency and reduces labor costs in agriculture. A custom dataset was created by collecting and labeling guava images. The YOLOv7-tiny model, trained with default parameters, achieved an initial mean Average Precision (mAP) of 66.7%. To improve accuracy, parameter adjustments, data augmentation (mosaic, mixup), and learning rate strategies (warm-up, decay) were employed, raising the mAP to 76.7%. The optimized model was transferred to mobile devices for convenient detection. This research provides an effective method for guava covering inspection and quantity counting, contributing to advancements in agricultural applications.