Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.

In this study, the critical factor of the chaos system has been analysed to improve the randomicity of the encryption keyspace. Several chaos systems have been integrated together with a linear function to form a much more efficient key sequence generator. This study also presents the cross colour field confusion method which scrambles the pixels among the R G B colour matrixes. In this way, the range of the pixels scrambling map is extended to three matrixes compared to the traditional pixels scrambling schemes which do pixels scrambling in its own colour matrix. The experiment results show that the improved cascade system has better bifurcation diagrams. The cross colour field diffusion algorithm makes the encrypted image has little pixel correlation and no information leakage. The experiment results justify that the novel cross colour confusion scheme with improved chaos systems has a good ability to resist brute force attack, known plain attack, chosen plain text attack, chosen ciphertext attack and differential attack. The security analysis demonstrates that the proposed approach has satisfactory properties in image encryption.

To address the design requirements of structural lightweighting and multi-performance coordination for UAV frames, an optimization method combining topology optimization and the NSGA-III multi-objective evolutionary algorithm is proposed. Twelve kinds of topological structures are obtained by setting different parameters through the Altair Inspire platform. A three-objective optimization model considering mass, stiffness, and strength was constructed to identify the Pareto-optimal solutions. Numerical analysis shows that the optimized design achieves approximately 59% mass reduction, with a maximum displacement of 0.2891 mm and a maximum stress of 4.449 MPa, meeting structural performance requirements. The results indicate that the proposed method effectively improves both the design efficiency and structural performance of UAV frames. The optimized models indicate favorable mechanical behavior and potential manufacturability; however, experimental manufacturing and structural testing are still required for full validation. The proposed workflow can be extended to the structural design of multirotor and fixed-wing UAV platforms.