Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109631 - 109631
Published: Nov. 23, 2024
Language: Английский
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109631 - 109631
Published: Nov. 23, 2024
Language: Английский
Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 304, P. 112449 - 112449
Published: Sept. 3, 2024
Language: Английский
Citations
4Information Processing in Agriculture, Journal Year: 2024, Volume and Issue: unknown
Published: May 1, 2024
In order to solve the problem of stability agricultural quadrotor working, its controller designing is first priority. Therefore, this paper makes an attempt use Radial Basis Function (RBF) neural network adaptive method combined with sliding mode control height channel. Validation efficacy RBF in conducted through simulation experiments utilizing parameters. The application has laid a theoretical foundation. At same time, experiments, it concluded theory that can have good prediction and elimination effect on interference during flight, change time constant will not affect aircraft. Notably, abrupt changes indicate UAV motor malfunction. Simulation results affirm proposed regulating altitude addressing sudden faults. Real-world experimentation reveals even when propellers sustain damage certain extent, hover capabilities remain intact. These findings provide solid groundwork for subsequent endeavors operations, while also offering innovative avenues advancing field.
Language: Английский
Citations
3Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129981 - 129981
Published: March 1, 2025
Language: Английский
Citations
0Drones, Journal Year: 2024, Volume and Issue: 8(9), P. 432 - 432
Published: Aug. 26, 2024
Remote sensing technology can be used to monitor changes in crop planting areas guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote is efficient, accurate, flexible, which quickly collect transmit high-resolution data real time precision agriculture management. It widely monitoring, yield prediction, irrigation However, the application of faces challenges such as a high imbalance land cover types, scarcity labeled samples, complex changeable coverage types long-term images, have brought great limitations monitoring cultivated changes. In order solve abovementioned problems, this paper proposed multi-scale fusion network (MSFNet) model based on input feature series further combined MSFNet Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) optimize parameters neural network. The method applied crops tomatoes. experimental results showed that average accuracy, F1-score, IoU optimized by PSO + MAML (PSML) were 94.902%, 91.901%, 90.557%, respectively. Compared with other schemes U-Net, PSPNet, DeepLabv3+, has better effect solving problem ground objects image samples provides technical support for subsequent technology. study found change different was closely related climatic conditions policies, helps use realization
Language: Английский
Citations
2Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: unknown, P. 100767 - 100767
Published: Dec. 1, 2024
Language: Английский
Citations
1Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112467 - 112467
Published: Nov. 16, 2024
Language: Английский
Citations
1Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109631 - 109631
Published: Nov. 23, 2024
Language: Английский
Citations
1