Multitarget Recognition of Flower Images Based on Lightweight Deep Neural Network and Transfer Learning

The colors and shapes of different flowers are similar, requiring an accurate and efficient multitarget recognition method to identify flower species, which can assist in agricultural automation for harvesting. This article utilizes deep neural networks and transfer learning methods, employing computer vision technology for flower recognition and classification. It compares the detection accuracy and speed of different algorithms to achieve real‐time multitarget detection of flowers. First, a lightweight target detection model based on YOLOv4 is proposed, replacing the original CSPDarknet53 with lightweight networks such as MobileNetV3 or CSPDarknet53_tiny. The results show that the lightweight target detection model achieves more than 30 frames per second and has a higher mAP value. Second, an image classification model based on strong‐supervised deep learning is established, using ShuffleNet networks and transfer learning techniques. During this process, 80 images generated by a generative adversarial network are added to enhance the model's generalization ability. The results indicate that, compared to training from scratch, transfer learning accelerates model convergence and improves prediction accuracy. The model under strong supervision classification demonstrates stronger generalization ability and robustness.