Estimation of Citrus Maturity with Florescence Spectroscopy Using Deep Learning

To produce high-quality citrus, the harvest time of citrus should be determined by considering its maturity. To evaluate citrus maturity, the Brix/acid ratio, which is the ratio of sugar content or soluble solids content to acid content, is one of the most commonly used indicators of fruit maturity. To estimate the Brix/acid ratio, fluorescence spectroscopy, which is a rapid, sensitive, and cheap technique, was adopted. Each citrus peel was extracted, and its fluorescence value was measured. Then, the fluorescent spectrum was analyzed using a convolutional neural network (CNN). In fluorescence spectroscopy, a matrix called excitation and emission matrix (EEM) can be obtained, in which each fluorescence intensity was recorded at each excitation and emission wavelength. Then, by regarding the EEM as an image, the Brix/acid ratio of juice from the flesh was estimated via performing a regression with a CNN (CNN regression). As a result, the Brix/acid ratio absolute error was estimated to be 2.48, which is considerably better than the values obtained by the other methods in previous studies. Hyperparameters, such as depth of layers, learning rate, and the number of filters used for this estimation, could be observed using Bayesian optimization, and the optimization contributed to the high accuracy.

[1]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[2]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[3]  Z. Jane Wang,et al.  A CNN Regression Approach for Real-Time 2D/3D Registration , 2016, IEEE Transactions on Medical Imaging.

[4]  Marco Locatelli,et al.  Bayesian Algorithms for One-Dimensional Global Optimization , 1997, J. Glob. Optim..

[5]  Jayme Garcia Arnal Barbedo,et al.  Factors influencing the use of deep learning for plant disease recognition , 2018, Biosystems Engineering.

[6]  Naoshi Kondo,et al.  Monitoring of Fluorescence Characteristics of Satsuma Mandarin (Citrus unshiu Marc.) during the Maturation Period , 2017 .

[7]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Tomoo Shiigi,et al.  Machine Vision System for Detecting Fluorescent Area of Citrus Using Fluorescence Image , 2010 .

[9]  Joris IJsselmuiden,et al.  Transfer learning for the classification of sugar beet and volunteer potato under field conditions , 2018, Biosystems Engineering.

[10]  Jonas Mockus,et al.  On Bayesian Methods for Seeking the Extremum , 1974, Optimization Techniques.

[11]  Fumiki Hosoi,et al.  Estimation of tree structure parameters from video frames with removal of blurred images using machine learning , 2018 .

[12]  Hanqi Zhang,et al.  Study of interaction between protein and main active components in Citrus aurantium L. by optical spectroscopy , 2010 .

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Yuting Zhang,et al.  Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yangqing Jia,et al.  Deep Convolutional Ranking for Multilabel Image Annotation , 2013, ICLR.

[16]  Ya‐Ping Sun,et al.  Bandgap-like strong fluorescence in functionalized carbon nanoparticles. , 2010, Angewandte Chemie.

[17]  M. Talón,et al.  Physiology of citrus fruiting , 2007 .

[18]  Haruhiko Murase,et al.  Machine vision based quality evaluation of Iyokan orange fruit using neural networks , 2000 .

[19]  Hushna Ara Naznin,et al.  Identification of a freshness marker metabolite in stored soybean sprouts by comprehensive mass-spectrometric analysis of carbonyl compounds. , 2018, Food chemistry.

[20]  Naoshi Kondo,et al.  Detection of Rotten Citrus Fruit Using Fluorescent Images , 2011 .

[21]  Federico Pallottino,et al.  Non-destructive Estimation of Mandarin Maturity Status Through Portable VIS-NIR Spectrophotometer , 2011 .

[22]  Neena Aloysius,et al.  A review on deep convolutional neural networks , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[23]  Pierre Alliez,et al.  Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[25]  Jayme G. A. Barbedo,et al.  Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification , 2018, Comput. Electron. Agric..

[26]  Brian Kingsbury,et al.  New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[27]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[28]  J. Christensen,et al.  Application of fluorescence spectroscopy and chemometrics in the evaluation of processed cheese during storage. , 2003, Journal of dairy science.

[29]  David C. Slaughter,et al.  Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence , 2008 .