Computer vision quality assessment of barley kernels

Transcription

1 University f Warmia and Mazury in Olsztyn Cmputer visin quality assessment f barley kernels Izabella Krczyńska 1, Aleksandra Jarsik 1, Pitr M. Szczypiński 1, Pitr Zaptczny 2 1 Institute f Electrnics, Ldz University f Technlgy 2 Department f Agri-Fd Prcess Engineering, University f Warmia and Mazury in Olsztyn EFITA/WCCA/CIGR Cnference, Pznań 2015 Supprted by the Natinal Centre fr Research and Develpment in Pland, grant n. PBS3/A8/38/2015

2 Visual Inspectin and Cmputer Visin 1) Visual inspectin is ne f the ldest and reliable fd quality assessment methds. Hwever, in industrial envirnment it is labr-expensive. 2) With the develpment f cmputer visin algrithms the human-expert can be assisted r replaced by an autmatic expert systems. 3) Cherent and ptimal methdlgy fr develping such systems is nt yet established. Nevertheless each case can be slved individually using unique, tailred algrithms. 2/17

3 Quality expectatins In the brewing industry, crucial fr the prductin f high quality malt is: kernel unifrmity, existence f germ in every kernel, lack f mechanical defects and lack f fungal infectins 3/17

4 Scpe f the Presentatin Gal and mtivatin Methds Material Image segmentatin Feature extractin Machine learning Data classificatin Crss-validatin Results Classificatin perfrmance Cnclusins 4/17

5 Gal and mtivatin Ecnmic Inspect mre Make inspectin quick Simplify training Pht: Dan DeLng/Seattle Pst-Intelligencer Technical Find reliable methd Imprve reprducibility 5/17

6 Material Images acquired with flatbed scanner. Samples f labeled kernels frm malt huse Sufflet Agr Plska. Classes: Gd fr malting Undevelped Cracked Spruted Spruted Swllen Missing germ Fusarium infected Other fungal infectins Fusarium infected 6/17

7 Image segmentatin Image segmentatin is t partitin an image int regins f interest suitable fr further analysis. Here, the regins may represent individual kernels. Our apprach: Grey-level threshlding Median filtering Identifying disjint areas 7/17

8 Feature extractin Every kernel is characterized by numerical variables features. Such features quantitatively describe shape, texture and clr. Every kernel described by a vectr f several-hundred features can be viewed as a crdinate in a severalhundred-dimensinal space. Space f three features vectrs f fusarium nrmal kernels 8/17

9 Machine learning Machine learning explres algrithms that frm example inputs create data-driven predictin mdels r decisins rules. Linear discriminant analysis is a methd t find a linear cmbinatin f features that separates tw r mre classes f bjects. The resulting cmbinatin is used as a linear classifier, in which a decisin bundary is simply a line (2D), plane (3D) r hyperplane. 9/17

10 Data classificatin Classificatin is a prcess f identifying t which categry (class) a new bservatin (kernel) belngs. Linear classifier ensembles are applied t deal with multi-categry identificatin. Gd Spruted Missing germ Fungal infectins... 10/17

11 Crss-validatin Crss-validatin is a technique fr estimating the perfrmance f a classifier. It invlves partitining data (set f feature vectrs) int subsets, perfrming machine learning n ne subset and validating the classifier n the ther ne. In this study the three-fld crss-validatin was applied: vectr sets were randmly divided int three pssibly equinumerus subsets tw subsets were used fr training and remaining ne fr validatin the prcess was repeated fr each f the three subsets used fr validatin 11/17

12 Sensitivity and specificity Sensitivity and specificity are statistical measures f the perfrmance f a binary classificatin test [wikipedia]. sensitivity = number f true psitives number f true psitives number f false negatives number f true negatives specificity= number f true negativesnumber f false psitives Real psitive Real negative True psitive False negative False psitive True negative Classified as psitive Classified as negative 12/17

13 Methds Barley images Classifier perfrmance Image segmentatin Feature extractin Linear discriminant analysis Three-fld validatin 13/17

14 Which side is better? Ventral Drsal Missing germ Gd fr malting Ventral 28 5 Missing germ 2 30 Gd fr malting Sensitivity: 0.93 Specificity: 0.86 Missing germ Gd fr malting Drsal 30 1 Missing germ 0 34 Gd fr malting Sensitivity: 1.00 Specificity: /17

15 Linear classifier perfrmance Missing germ Gd fr malting Ventral 30 1 Missing germ 0 34 Gd fr malting Fusarium Gd fr malting Ventral 40 4 Fusarium 2 31 Gd fr malting Sensitivity: 1.00 Specificity: 0.97 Sensitivity: 0.95 Specificity: 0.89 Fungal infectin Gd fr malting Drsal 52 6 Fungal infectin 2 29 Gd fr malting Sensitivity: 0.96 Specificity: /17

16 Classifier ensemble perfrmance Swllen Fusarium Fungal inf. Mis. germ Gd Drsal Swllen Fusarium Fungal inf Mis. germ Gd Unassigned Swllen Fusarium Fungal inf. Mis. germ Gd Ventral Swllen Fusarium Fungal inf Mis. germ Gd Unassigned 16/17

17 Cnclusins Cmputer visin methds can identify barley kernels suitable r unsuitable fr malting. Cmbinatin f image segmentatin, feature extractin and machine learning methds des the jb. Accuracy f classificatin (the average f sensitivity and specificity) ranges frm 0.88 t 0.98 fr linear classifiers and reaches 0.72 fr classifier ensembles. Analysis f drsal r ventral side f kernels has impact n classificatin results. 17/17