吴亚榕,李键红.基于联合稀疏模型的黄瓜病害自动识别[J].湖南农业大学学报:自然科学版,2019,45(4):.
基于联合稀疏模型的黄瓜病害自动识别
  
DOI:
中文关键词:  黄瓜病害识别  多任务学习  联合稀疏模型  加速近端梯度  图像分割  特征抽取
英文关键词:cucumber disease recognition  multi-task learning  joint sparse model  accelerated proximal gradient  image segmentation  feature extraction
基金项目:国家自然科学基金项目(61877013);广东省自然科学基金项目(2017A030310618);广东省科学技术厅项目(2016A020210131);广东省重点平台及科研项目(2017GXJK073)
作者单位
吴亚榕,李键红 1.仲恺农业工程学院机电学院广东 广州 5102252.广东外语外贸大学语言工程与计算实验室广东 广州 510006 
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中文摘要:
      提取黄瓜7种叶部病害图像颜色、形状和纹理的共26种特征进行研究,发现不同形式的特征在用同一样本集合稀疏表示时,它们的稀疏系数有着相似的结构。通过引入联合稀疏模型构造方程,对这一规律进行数学描述,使用加速近端梯度法求解联合稀疏系数,最后借助重构误差来实现病害识别。试验表明,这一算法的正确识别率达到90.67%,较稀疏表示分类算法提高5.7%,计算消耗时间7.5 s,较稀疏表示分类算法缩短4.3 s。
英文摘要:
      Twenty-six color, shape and texture features were extracted from seven kinds of cucumber disease leaf. It was found that the sparsity coefficients for different features had similar structures when they were sparse represented by the same training set. By introducing the joint sparse model to construct the cost equation, thus the regularity was summarized in mathematics. The joint sparse coefficients were solved by using the accelerated proximal gradient method. Finally, disease recognition was realized by means of reconstruction error. Experiments demonstrated that the correct recognition rate of this algorithm reaches 90.67%, which is 5.7% higher than that of the sparse representation classification algorithm, and the computational consumption time is 7.5 s, shortening 4.3 s than that of the sparse representation classification algorithm.
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