Abstract A lightweight fruit detection algorithm is important to ensure real‐time detection on low‐power computing devices while maintaining detection accuracy. In addition, the fruit detection algorithm is also faced with some environmental factors. To solve these challenges, lightweight detection algorithms termed YOLO‐Lite, YOLO‐Liter and YOLO‐Litest were developed based on the YOLOv5 framework. The compared mean average precision (mAP) detection revealed that YOLO‐Lite at 0.86 is 2%, 4%, 5%, 7%, and 16% more than YOLO‐Liter and YOLOv5n at 0.84 each, YOLOv4‐tiny at 0.82, YOLO‐Liter at 0.81, YOLO‐MobileNet at 0.79, and YOLO‐ShuffleNet at 0.70, respectively, but not for YOLOv8n at 0.87. On the Computer platform, except for YOLOv4‐tiny at 178.6 frames per second (FPS), the speed of YOLO‐Litest at 158.7 FPS is faster than YOLO‐Liter at 129.9 FPS, YOLO‐Lite at 120.5 FPS, YOLO‐ShuffleNet at 119.0 FPS, YOLOv8n at 116 FPS, YOLOv5n at 111.1 FPS, and YOLO‐MobileNet at 89.3 FPS. Using Jetson Nano, the 32.3 FPS of YOLO‐Litest is faster than other algorithms, but not YOLOv4‐tiny's 34.1 FPS. On the Raspberry Pi 4B, YOLO‐Litest with 4.69 FPS, outperformed other algorithms. The choices for an accurate and faster detection algorithm are YOLO‐Lite and YOLO‐Litest respectively, while YOLO‐Liter maintains a balance between them.

On the basis of the chaotic features of the frequency hopping signal, frequency band prediction for frequency hopping signal can enhance the interference effect of the signal greatly. However, poor prediction accuracy often limits its development in the military field. Therefore, for the sake of enhancing the frequency band prediction accuracy of frequency hopping signal, this paper studies the radial basis function (RBF) neural network frequency hopping signal frequency band prediction model based on the gradient descent method and improved the particle swarm optimization algorithm, respectively. The former uses a step‐by‐step algorithm to optimize the center value and weight so that the network can find the most suitable initial state. Then, the clustering selection optimization algorithm is employed to optimize the central value. In addition, it optimizes the weight by using a gradient descent method of the optimal learning rate. The latter optimizes the structure of the RBF neural network through the combination of the subtractive clustering algorithm and improved the particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the gradient RBF algorithm model performs better in terms of accuracy, but time efficiency is lower, while the PSO‐RBF algorithm has better time efficiency.