- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach
摘要： Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.
关键词： image classifiers,machine learning system,precision agriculture,recognition system,mutual subspace method
更新于2025-09-19 17:15:36
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Target recognition system of dynamic scene based on artificial intelligence vision
摘要： Using the current recognition system to recognize dynamic scene cannot effectively speed up the target recognition. When target recognition increases, the accuracy of target recognition is relatively low. In order to solve this problem, a target recognition system of dynamic scene based on DSP was designed. Combined with the idea of DSP system design, the design process and composition of target recognition system was expounded. The recognition algorithm based on spatial-temporal condition information was used to realize the designed recognition system. By introducing the visual attention mechanism, the spatial-temporal domain model based on visual significance was built. The pixel neighborhood weighted condition information was used as classification features to enhance the linear separability for target and background and improve the recognition accuracy of dynamic scene moving target. Finally, combined with image block modeling strategy, the efficient and real-time recognition of moving target in dynamic scene was realized. Experimental results show that the proposed target recognition system can effectively improve the accuracy of target recognition.
关键词： Artificial intelligence vision,recognition system,dynamic scene,target recognition
更新于2025-09-10 09:29:36
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Design and Implementation of Cherry Picking Robot Vision Recognition System Based on Face Recognition
摘要： This paper analyzes the necessity of cherry picking robot vision recognition system. It introduces the system function and the system structure, and provides the system operation environment, the development platform and the database design. This paper designs and implements the cherry picking robot vision recognition system, based on C# technology, using MATLAB, using face recognition technology.
关键词： Cherry picking robot,Vision recognition system,face recognition
更新于2025-09-09 09:28:46