In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, ...In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.展开更多
在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPP...在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPPT算法,该算法通过改变粒子群算法(particle swarm algorithm,PSO)的惯性系数和两个学习因子,使其随着迭代进行非线性动态变化,同时引入变异策略,增强算法的全局寻优能力,达到了提升搜索精度和速度的目的。在Matlab/Simulink中建立了仿真模型,验证了改进的粒子群算法在随机光照强度能有效保证输出功率最大。展开更多
基金Supported by Natural Science Foundation of Shandong Province(ZR2021MF096)Shandong Agricultural Machinery Equipment R&D Innovation Planning Project (2018YF009)。
文摘In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.
文摘在光伏发电系统中,光伏组件会受到实时变化光照强度的影响而处于局部阴影下,光伏组件的输出特性曲线呈现多峰值状态分布,传统的最大功率点跟踪方法(maximum power tracking,MPPT)会失效,造成系统输出功率的损失。本文提出一种改进的MPPT算法,该算法通过改变粒子群算法(particle swarm algorithm,PSO)的惯性系数和两个学习因子,使其随着迭代进行非线性动态变化,同时引入变异策略,增强算法的全局寻优能力,达到了提升搜索精度和速度的目的。在Matlab/Simulink中建立了仿真模型,验证了改进的粒子群算法在随机光照强度能有效保证输出功率最大。
