In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples.The method provides a novel way of using fo...In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples.The method provides a novel way of using four images(top,bottom and two sides)and average gray values for each apple to distinguish between the apple defects,stem and calyx.Furthermore,weighted features(MCSAD(maximum cross-sectional average diameter),circularity,PRA(proportion of red area)and defect regions)were carefully selected according to the requirements of the national apple grading standard,which improves the practicality of the proposed method.Finally,qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%,which provides encouragement for the additional research and implementation of multifeature automatic grading for the fruit industry.展开更多
This article reviews the state of the art of recent CNN models used for external quality inspection of fruits,considering parameters such as color,shape,size,and defects,used to categorize fruits according to internat...This article reviews the state of the art of recent CNN models used for external quality inspection of fruits,considering parameters such as color,shape,size,and defects,used to categorize fruits according to international marketing levels of agricultural products.The literature review considers the number of fruit images in different datasets,the type of images used by the CNN models,the performance results obtained by each CNNs,the optimizers that help increase the accuracy of these,and the use of pre-trained cNN models used for transfer learning.CNN models have used various types of images in the visible,infrared,hyperspectral,and multispectral bands.Furthermore,the fruit image datasets used are either real or synthetic.Finally,several tables summarize the articles reviewed,which are prioritized according to inspection parameters,facilitating a critical comparison of each work.展开更多
基金This research was mainly supported by the Collaborative Innovation Center of Henan Grain Crops,Zhengzhou and by the National Key research and development programof China(No.2017YFD0301105)Key science and Technology Program of Henan Province(No.192102110196).
文摘In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples.The method provides a novel way of using four images(top,bottom and two sides)and average gray values for each apple to distinguish between the apple defects,stem and calyx.Furthermore,weighted features(MCSAD(maximum cross-sectional average diameter),circularity,PRA(proportion of red area)and defect regions)were carefully selected according to the requirements of the national apple grading standard,which improves the practicality of the proposed method.Finally,qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%,which provides encouragement for the additional research and implementation of multifeature automatic grading for the fruit industry.
文摘This article reviews the state of the art of recent CNN models used for external quality inspection of fruits,considering parameters such as color,shape,size,and defects,used to categorize fruits according to international marketing levels of agricultural products.The literature review considers the number of fruit images in different datasets,the type of images used by the CNN models,the performance results obtained by each CNNs,the optimizers that help increase the accuracy of these,and the use of pre-trained cNN models used for transfer learning.CNN models have used various types of images in the visible,infrared,hyperspectral,and multispectral bands.Furthermore,the fruit image datasets used are either real or synthetic.Finally,several tables summarize the articles reviewed,which are prioritized according to inspection parameters,facilitating a critical comparison of each work.
