Abstract The most important quality parameter of a product is its nutritional value, but marketability of agricultural products depends primarily on the overall appearance and shape of the products. This study was carried out with the aim of developing cherry fruit packing methods and thus reducing waste and increasing its exportability and marketability. Therefore, the purpose of research was to use the improved Convolutional Neural Network (CNN) algorithm to detect the appearance of cherries and provide an efficient system for their grading. In order to identify and classify images cherry on two classes (regular and irregular shaped) was prepared. After preprocessing the images, the proposed method utilized its ability to improve generalization in the CNN through a combination of max pooling and average pooling techniques, to grade cherries. In order to compare the proposed method (CNN) with HOG and LBP methods, the properties of the images extracted by KNN, ANN, Fuzzy and Ensemble Decision Trees (EDT) algorithms were categorized. The proposed method based on hybrid pooling is also compared with CNN with baseline pooling method such as average pooling. Comparisons based on the results of simulation demonstrate the superiority of the proposed improved CNN over other methods presenting an accuracy of 99.4 %. Therefore, the CNN and image processing methods are effective in managing the marketability and exportability of the cherry fruit and can replace the traditional methods applied for grading cherries.

中文翻译：

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基于混合池化方法的深度CNN对樱桃果实的准确分类

摘要 产品最重要的质量参数是其营养价值，但农产品的适销性主要取决于产品的整体外观和形状。开展这项研究的目的是开发樱桃水果包装方法，从而减少浪费并提高其出口性和适销性。因此，研究的目的是使用改进的卷积神经网络（CNN）算法来检测樱桃的外观并为其分级提供一个有效的系统。为了对图像进行识别和分类，准备了两个类别（规则形状和不规则形状）的樱桃。在对图像进行预处理后，所提出的方法利用其通过最大池化和平均池化技术的组合提高 CNN 中的泛化能力，对樱桃进行分级。为了将所提出的方法 (CNN) 与 HOG 和 LBP 方法进行比较，对 KNN、ANN、模糊和集成决策树 (EDT) 算法提取的图像的属性进行了分类。所提出的基于混合池化的方法也与具有基线池化方法（例如平均池化）的CNN进行了比较。基于模拟结果的比较证明了所提出的改进 CNN 优于其他方法，其精度为 99.4%。因此，CNN 和图像处理方法在管理樱桃果实的适销性和出口性方面是有效的，可以替代传统的樱桃分级方法。对模糊和集成决策树 (EDT) 算法进行了分类。所提出的基于混合池化的方法也与具有基线池化方法（例如平均池化）的CNN进行了比较。基于模拟结果的比较证明了所提出的改进 CNN 优于其他方法，其精度为 99.4%。因此，CNN 和图像处理方法在管理樱桃果实的适销性和出口性方面是有效的，可以替代传统的樱桃分级方法。对模糊和集成决策树 (EDT) 算法进行了分类。所提出的基于混合池化的方法也与具有基线池化方法（例如平均池化）的CNN进行了比较。基于模拟结果的比较证明了所提出的改进 CNN 优于其他方法，其精度为 99.4%。因此，CNN 和图像处理方法在管理樱桃果实的适销性和出口性方面是有效的，可以替代传统的樱桃分级方法。