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Traditional agricultural practices of hand-picking ripe tomatoes are labor-intensive and inefficient for large-scale harvesting. To address this, we propose an innovative approach using the YOLOv7 algorithm for ripe tomato detection, enabling robotic arms to perform the picking. However, the occlusion of tomatoes in the field often leads to unclear target features, causing false or missed detections. So it is worth studying and this paper proposes a tomato detection method based on improved YOLOv7. The novelty is shown below. First, a new structure called ReplkDext is redesigned to increase the receptive field. ReplkDext is introduced before the last layer of CBS in the backbone. Secondly, to overcome the problem of low FLOPS caused by frequent access to memory in traditional neural networks, the head structure of YOLOv7 is redesigned. By using FasterNet to optimize the structure between Concat and CBS in the head, FasterNet makes the model balance between running speed and detection accuracy. Finally, to improve the ability of convolution, ODConv is added after the last ELANN-2 structure in the Head layer. ODConv improves the feature extraction ability of small targets and obtains more feature information about ripe tomatoes. Experiments show that compared with YOLOv7, Map@.5 of Tomato-YOLOv7 has increased by 1.3%. The model is overall better than other models. The overall contribution of the Tomato-YOLO model is to provide important insights into agricultural product detection and provide a theoretical basis for automated tomato harvesting in orchards.

Revolutionizing Agriculture: Real-Time Ripe Tomato Detection with the Enhanced Tomato-YOLOv7 System