Strawberry is one of the popular fruits with numerous nutrients.The ripeness of this fruits was estimated using the hyperspectral imaging(HSI)system in field and laboratory conditions in this study.Strawberry at early...Strawberry is one of the popular fruits with numerous nutrients.The ripeness of this fruits was estimated using the hyperspectral imaging(HSI)system in field and laboratory conditions in this study.Strawberry at early ripe and ripe stageswere collected HSI data,coveredwavelength ranges from370 to 1015 nm.Spectral featurewavelengths were selected using the sequential feature selection(SFS)algorithm.Two wavelengths selected for field(530 and 604 nm)and laboratory(528 and 715 nm)samples,respectively.Then,reliability of such spectral featureswas validated based on support vectormachine(SVM)classifier.Performance of SVMclassification models had good resultswith receiver operating characteristic values for samples under both field and laboratory conditions higher than 0.95.Meanwhile,the spatial feature images were extracted from the spectral feature wavelength and the first three principal components for laboratory samples.Pretrained AlexNet convolutional neural network(CNN)was used to classify the early ripe and ripe strawberry samples,which obtained the accuracy of 98.6%for test dataset.The above results indicated real-time HSI system was promising for estimating strawberry ripeness under field and laboratory conditions,which could be a potential application technique for evaluating the harvesting time management for farmers and producers.展开更多
基金This researchwas supported by National Natural Science Foundation of China(Nos.31701325,31671632)This work was also funded by China Scholarship Council(No.201709135004)Post-doctor Fund of Jiangsu Province.
文摘Strawberry is one of the popular fruits with numerous nutrients.The ripeness of this fruits was estimated using the hyperspectral imaging(HSI)system in field and laboratory conditions in this study.Strawberry at early ripe and ripe stageswere collected HSI data,coveredwavelength ranges from370 to 1015 nm.Spectral featurewavelengths were selected using the sequential feature selection(SFS)algorithm.Two wavelengths selected for field(530 and 604 nm)and laboratory(528 and 715 nm)samples,respectively.Then,reliability of such spectral featureswas validated based on support vectormachine(SVM)classifier.Performance of SVMclassification models had good resultswith receiver operating characteristic values for samples under both field and laboratory conditions higher than 0.95.Meanwhile,the spatial feature images were extracted from the spectral feature wavelength and the first three principal components for laboratory samples.Pretrained AlexNet convolutional neural network(CNN)was used to classify the early ripe and ripe strawberry samples,which obtained the accuracy of 98.6%for test dataset.The above results indicated real-time HSI system was promising for estimating strawberry ripeness under field and laboratory conditions,which could be a potential application technique for evaluating the harvesting time management for farmers and producers.
