Classification of ripening stages of bananas based on support vector machine

Non-destructive quality detection and automatic grading are important in fruit industry.The traditional way divides bananas into 7-level ripening stages based on color.This study investigated the changes of peel color at three positions of banana fingers,i.e.stalk,middle and tip.A support vector machine method was used to classify the ripening stages by color value L*,a*and b*as input data.The ripening stages were classified by 10-fold cross validation method of support vector machines with radial basis function kernel and linear function kernel.The results showed that the color change of middle position of banana finger adequately reflected the changes in banana ripening stages.a*value continuously increased from ripening stage 1 to ripening stage 7,L*and b*values increased from ripening stage 1 to ripening stage 5,and then decreased from ripening stage 5 to ripening stage 7.It was difficult to recognize the ripening stages using L*,a*and b*values individually.The accuracy of classification using support vector machine based on radial basis function kernel reached 96.5%,which was higher than that for linear function kernel.This research can provide a reference for automatic classification of banana ripening stages.

Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

Fruit processing devices,that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture.Recently,based on deep learning,many attempts have been made in computer image processing,to monitor the ripening stage of fruits.However,it is timeconsuming to acquire images of the various ripening stages to be used for training,and it is difficult to measure the ripening stages of fruits accurately with a small number of images.In this paper,we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks(DNNs)and machine learning(ML)that focus on optimizing them in combination on several image datasets.First,we used eight DNN algorithms to extract the color feature vectors most suitable for classifying them from the observed images representing each ripening stage.Second,we applied seven ML methods to determine the ripening stage of fruits based on the extracted color features.Third,we propose an automatic prediction system that can accurately determine the ripeness in images of various fruits such as strawberries and tomatoes by a combination of the DNN and ML methods.Additionally,we used the transfer learning method to train the proposed system on few image datasets to increase the training speed.Fourth,we experimented to find out which of the various combinations of DNN and ML methods demonstrated excellent classification performance.From the experimental results,a combination of DNNs and multilayer perceptron,or a combination of DNNs and support vector machine or kernel support vector machine generally exhibited excellent classification performance.Conversely,the combination of various DNNs and statistical classification models shows that the overall classification rate is low.Second,in the case of using tomato images,it was found that the classification rate for the combination of various DNNs and ML methods was generally similar to the results obtained for strawberry images.