A quantitative multivariate methodology for unsupervised class identification in pistachio (Pistacia vera L.) plant leaves size
Abstract
Aim of study: Genetic diversity of pistachio, can be evaluated by using different descriptors, as adopted in international certification systems. Mainly the descriptors are morphological traits as leaf, which represents an important organ for its sensibility to growth conditions during the expansion phase. This study adopted a rapid and quantitative non-hierarchic clustering classification (k-means), to extract size classes basing on the contemporary combination of different morphological traits (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) of a varietal collection composed by 21 pistachio cultivars.
Area of study: Worldwide.
Material and methods: The unsupervised non-hierarchic clustering technique was adopted to the entire samples of pistachio leaves from k=2 to k=15 for both four morphological variables (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) and three morphological variables (i.e., terminal leaf length, terminal leaf width and terminal leaf ratio).
Main results: A classification model only on the three morphological variables (for results of statistical analysis in which the groups resulted to be more separated and different for all the variables), with k= 5 (five groups), was constructed using a non-linear artificial neural network approach. The percentages of bad prediction in both training and testing resulted equal to 0%. The “terminal leaf length” returned the higher impact (44.89%).
Research highlights: The contemporary combination of different morphological leaf traits, allowed to create an automatic classification of size classes of great importance for cultivar identification and comparison.
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References
Abdou HA, Kuzmic A, Pointon J, Lister RJ, 2012. Determinants of capital structure in the UK retail industry: A comparison of multiple regression and generalized regression neural network. Intell Syst Account Finance Manag 19 (3): 151-169. https://doi.org/10.1002/isaf.1330
Antonucci F, Costa C, Pallottino F, Paglia G, Rimatori V, De Giorgio D, Menesatti P, 2012. Quantitative method for shape description of almond cultivars (Prunus amygdalus Batsch). Food Bioprocess Technol 5: 768-785. https://doi.org/10.1007/s11947-010-0389-2
Arribas JI, Sánchez-Ferrero GV, Ruiz-Ruiz G, Gómez-Gil J, 2011. Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agric 78 (1): 9-18. https://doi.org/10.1016/j.compag.2011.05.007
Asma BM, Ozturk K, 2005. Analysis of morphological, pomological and yield characteristics of some apricot germplasm in Turkey. Genet Resour Crop Evol 52: 305-313. https://doi.org/10.1007/s10722-003-1384-5
Ayad WG, 1986. Conservation of crop germplasm: an overview of the FAO/IBPGR regional programme for South West Asia. Proc R Soc Edinb B 89: 265-271. https://doi.org/10.1017/S0269727000009088
Bacchetta L, Rovira M, Tronci C, Aramini M, Drogoudi P, Silva AP, Solar A, Avanzato D, Botta R, Valentini N, Boccacci P, 2015. A multidisciplinary approach to enhance the conservation and use of hazelnut Corylus avellana L. genetic resources. Genet Resour Crop Evol 62: 649-663. https://doi.org/10.1007/s10722-014-0173-7
Badenes ML, Martinez-Calvo J, Llacer G, 1998. Analysis of apricot germplasm from the European ecogeographical group. Euphytica 102: 93-99. https://doi.org/10.1023/A:1018332312570
Balduzzi M, Binder BM, Bucksch A, Chang C, Hong L, Iyer-Pascuzzi AS, Pradal C, Sparks EE, 2017. Reshaping plant biology: qualitative and quantitative descriptors for plant morphology. Front Plant Sci 8: 117. https://doi.org/10.3389/fpls.2017.00117
Bassil N, Hummer KE, Postman JD, Fazio G, Baldo A, Armas I, Williams R, 2009. Nomenclature and genetic relationships of apples and pears from Terceira Island. Genet Resour Crop Evol 56: 339-352. https://doi.org/10.1007/s10722-008-9369-z
Bayramzadeh V, Funada R, Kubo T, 2008. Relationships between vessel element anatomy and physiological as well as morphological traits of leaves in Fagus crenata seedlings originating from different provenances. Trees 22: 217-224. https://doi.org/10.1007/s00468-007-0178-3
Belhadj S, Derridj A, Aigouy T, Gers C, Gauquelin T, Mevy JP, 2007. Comparative morphology of leaf epidermis in eight populations of Atlas pistachio (Pistacia atlantica Desf., Anacardiaceae). Microsc Res Tech 70 (10): 837-846. https://doi.org/10.1002/jemt.20483
Berthaud J, 1997. Strategies for conservation of genetic resources in relation with their utilization. Euphytica 96: 1-12. https://doi.org/10.1023/A:1002922220521
Chao CT, Parfitt DE, Ferguson L, Kallsen C, Maranto J, 2003. Genetic analyses of phenological traits of pistachio (Pistacia vera L.). Euphytica 129: 345-349. https://doi.org/10.1023/A:1022206911350
Chatti K, Choulak S, Guenni K, Salhi-Hannachi A, 2017. Genetic diversity analysis using morphological parameters in Tunisian Pistachio (Pistacia vera L.). Int Res J Biol Sci 2: 29-34.
Chitwood DH, Ranjan A, Martinez CC, Headland LR, Thiem T, Kumar R, et al., 2014. A modern ampelogray: a genetic basis for leaf shape and venation patterning in grape. Plant Physiol 164 (1): 259-272. https://doi.org/10.1104/pp.113.229708
Chitwood DH, Otani WG, 2017. Morphometric analysis of Passiflora leaves: the relationships between landmarks of the vasculature and elliptic Fourier descriptors of the blade. GigaScience 6: 1-13. https://doi.org/10.1093/gigascience/giw008
Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun DW, Menesatti P, 2011. Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol 4: 673-692. https://doi.org/10.1007/s11947-011-0556-0
Crane JC, 1978. Pistachio tree nuts. Avipublishing Co., Westport, California.
Currie AJ, Ganeshanandam S, Noiton DA, Garrick D, Shelbourne CJA, Oraguzie N, 2000. Quantitative evaluation of apple (Malus x domestica Borkh.) fruit shape by principal component analysis and Fourier descriptors. Euphytica 111: 219-227. https://doi.org/10.1023/A:1003862525814
FAOSTAT, 2017. FAO web page. http://www.fao.org/faostat [30 March 2020].
Fares K, Guasmi F, Touil L, Triki T, Ferchichi A, 2009. Genetic diversity of pistachio tree using inter-simple sequence repeat markers ISSR supported by morphological and chemical markers. Biotechnol 8: 24-34. https://doi.org/10.3923/biotech.2009.24.34
Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters 27 (8): 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Febbi P, Menesatti P, Costa C, Pari L, Cecchini M, 2015. Automated determination of poplar chip size distribution based on combined image and multivariate analyses. Biomass Bioenergy 73: 1-10. https://doi.org/10.1016/j.biombioe.2014.12.001
Furuta N, Ninomiya S, Takahashi S, Ohmor H, Ukai Y, 1995. Quantitative evaluation of soybean (Glycine max L., Merr.) leaflet shape by principal component scores based on elliptic Fourier descriptor. Breed Sci 45 (3): 315-320. https://doi.org/10.1270/jsbbs1951.45.315
Gharaghani A, Solhjoo S, Oraguzie N, 2017. A review of genetic resources of almonds and stone fruits (Prunus spp.) in Iran. Genet Resour Crop Evol 64: 611-640. https://doi.org/10.1007/s10722-016-0485-x
Goto S, Iwata H, Shibano S, Ohya K, Suzuki A, Ogawa H, 2005. Fruit shape variation in Fraxinus mandshurica var. japonica characterized using elliptic Fourier descriptors and the effect on flight duration. Ecol Res 20: 733-738. https://doi.org/10.1007/s11284-005-0090-5
Guan X, Zhang L, Huang S, Peng Z, 2020. Infrared small target detection via non-convex tensor rank surrogate joint local contrast energy. Remote Sens 12(9): 1520. https://doi.org/10.3390/rs12091520
Hammer Ø, Harper DAT, Ryan PD, 2001. Past: paleontological statistics software package for education and data analysis. Palaeontol Electron 4: 1-9.
Hassoon IM, Kassir SA, Altaie SM, 2018. A review of plant species identification techniques. Int Jour Sci Res 7 (8): 2319-7064.
Höfer M, Eldin Ali MAMS, Sellmann J, Peil A, 2014. Phenotypic evaluation and characterization of a collection of Malus species. Genet Resour Crop Evol 61: 943-964. https://doi.org/10.1007/s10722-014-0088-3
Höfer M, Peil A, 2015. Phenotypic and genotypic characterization in the collection of sour and duke cherries (Prunus cerasus and x P. x gondouini) of the Fruit Genebank in Dresden-Pillnitz, Germany. Genet Resour Crop Evol 62 (4): 551-566. https://doi.org/10.1007/s10722-014-0180-8
Hormaza JI, Pinney K, Polito VS, 1998. Genetic diversity of pistachio (Pistacia vera, Anacardiaceae) germplasm based on randomly amplified polymorphic DNA (RAPD) markers. Econ Bot 52: 78-87. https://doi.org/10.1007/BF02861298
IPGRI, 1997. Descriptors for pistachio (Pistacia vera L.). International Plant Genetic Resources Institute. Rome, Italy.
Iwata H, Niikura S, Matsuura S, Takano Y, Ukai Y, 1998. Evaluation of variation of root shape of Japanese radish (Raphanus sativus L.) based on image analysis using elliptic Fourier descriptors. Euphytica 102: 143-149. https://doi.org/10.1023/A:1018392531226
Iwata H, Ebana K, Uga Y, Hayashi T, 2015. Genomic prediction of biological shape: elliptic Fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.). PLoS One 10 (3): e0120610. https://doi.org/10.1371/journal.pone.0120610
Jensen RJ, Ciofani KM, Miramontes LC, 2002. Lines, outlines, and landmarks: morphometric analyses of leaves of Acer rubrum, Acer saccharinum (Aceraceae) and their hybrid. Taxon 51: 475-492. https://doi.org/10.2307/1555066
Kadir A, 2015. Leaf identification using Fourier descriptors and other shape features. Gate to Computer Vision and Pattern Recognition 1: 3-7. https://doi.org/10.15579/gtcvpr.0101.003007
Kafkas S, Kafkas E, Perl-Treves R, 2002. Morphological diversity and a germplasm survey of three wild Pistacia species in Turkey. Genet Resour Crop Evol 49 (3): 261-270. https://doi.org/10.1023/A:1015563412096
Karimi HR, Zamani Z, Ebadi A, Fatahi MR, 2009. Morphological diversity of Pistacia species in Iran. Genet Resour Crop Evol 56 (4): 561-571. https://doi.org/10.1007/s10722-008-9386-y
Kashaninejad M, Tabil LG, 2011. Pistachio (Pistacia vera L.). In: Postharvest biology and technology of tropical and subtropical fruits, vol 4; Yahia EM (ed). Woodhead Publ Ltd, Cambridge, UK (pp. 218-246). https://doi.org/10.1533/9780857092618.218
Kennard RW, Stone LA, 1969. Computer aided design of experiments. Technometrics 11: 137-148. https://doi.org/10.1080/00401706.1969.10490666
Lin TS, Crane JC, Ryugo K, Polito VS, Dejong TM, 1984. Comparative study of leaf morphology, photosynthesis and leaf conductance in selected Pistacia species. J Am Soc Hortic Sci 109: 325-330.
Lootens L, Chaves B, Bert J, Pannecoucque J, Van Waes J, Roldan-Ruiz I, 2013. Comparison of image analysis and direct measurement of UPOV taxonomic characteristics for variety discrimination as determined over five growing season, using industrial chicory as a model top. Euphytica 189: 329-341. https://doi.org/10.1007/s10681-012-0750-9
López-Santos A, Page T, 2018. Elliptic Fourier descriptors of leaf outlines: a tool to discriminate among Aquilaria species (Thymelaeaceae). Silvae Genetica 67: 89-92. https://doi.org/10.2478/sg-2018-0012
Massimo L, Laura D, Ginevra LB, 2020. Phytosterols and phytosterol oxides in Bronte's pistachio (Pistacia vera L.) and in processed pistachio products. Eur Food Res Technol 246 (2): 307-314. https://doi.org/10.1007/s00217-019-03343-8
Mossalam A, Arafa M, 2017. Using artificial neural networks (ANN) in projects monitoring dashboards' formulation. HBRC J 14 (3): 385-392. https://doi.org/10.1016/j.hbrcj.2017.11.002
Neto JC, Meyer GE, Jones DD, Samal AK, 2006. Plant species identification using Elliptic Fourier leaf shape analysis. Comput Electron Agr 50 (2): 121-134. https://doi.org/10.1016/j.compag.2005.09.004
Olsson Å, Nybom H, Prentice HC, 2000. Relationships between Nordic dogroses (Rosa L. sect. Caninae, Rosaceae) assessed by RAPDs and elliptic Fourier analysis of leaflet shape. Syst Bot 25 (3): 511-521. https://doi.org/10.2307/2666693
Palisade Knowledge Base, 2020. 15.36. Calculation and use of variable impacts. https://kb.palisade.com/index.php?pg=kb.page&id=225 [29 June 2020].
Pazouki L, Mardi M, Shanjani PS, Hagidimitriou M, Pirseyedi SM, Naghavi MR, Avanzato D, Vendramin E, Kafkas S, Ghareyazie B, et al., 2010. Genetic diversity and relationships among Pistacia species and cultivars. Conserv Genet 11: 311-318. https://doi.org/10.1007/s10592-009-9812-5
Proto AR, Sperandio G, Costa C, Maesano M, Antonucci F, Macrì G, Scarascia Mugnozza G, Zimbalatti G, 2020. A three-step neural network artificial intelligence modelling approach for time, productivity and costs prediction: a case study in Italian forestry. Croat J For Eng 41: 35-47. https://doi.org/10.5552/crojfe.2020.611
Ray S, Turi RH, 1999. Determination of number of clusters in k-means clustering and application in colour image segmentation. Proc 4th Int Conf on Advances in Pattern Recognition and Digital Techniques (ICAPRDT'99), Calcutta, India, pp: 137-143.
Sabzi S, Pourdarbani R, Arribas JI, 2020. A computer vision system for the automatic classification of five varieties of tree leaf images. Computers 9 (1): 6. https://doi.org/10.3390/computers9010006
Scheldeman X, Van Damme P, Romero Motoche J, Urena Alvarez JV, 2006. Germplasm collection and fruit characterization of cherimoya (Annona cherimola) in Loja province, Ecuador, an important centre of biodiversity. Belg J Bot 139 (1): 27-38.
Sheikhi A, Arab MM, Brown PJ, Ferguson L, Akbari M, 2019. Pistachio (Pistacia spp.) breeding. In: Advances in plant breeding strategies: nut and beverage crops; Al-Khayri J, Jain S, Johnson D (eds). Springer, Cham, pp. 353-400. https://doi.org/10.1007/978-3-030-23112-5_10
Sun DW, Costa C, Menesatti P, 2012. Advantages of using quantitative shape descriptors in protocols for plant cultivar and postharvest product quality assessment. Food Bioprocess Technol 5: 1-2. https://doi.org/10.1007/s11947-011-0715-3
UPOV, 2020. International Union for the Protection of New Varieties of Plants. https://www.upov.int/edocs/mdocs/upov/en/twf_50/tg_pista_proj_3.pdf [30 March 2020].
Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL, 2007. A leaf recognition algorithm for plant classification using probabilistic neural network. 2007 IEEE Int Symp on Signal Processing and Information Technology, pp: 11-16. https://doi.org/10.1109/ISSPIT.2007.4458016
Yoshioka Y, Iwata H, Ohsawa R, Ninomiya S, 2004. Analysis of petal shape variation of Primula sieboldii by elliptic Fourier descriptors and principal component analysis. Ann Bot 94 (5): 657-664. https://doi.org/10.1093/aob/mch190
Zha H, He X, Ding C, Gu M, Simon HD, 2002. Spectral relaxation for k-means clustering. Neural Inform Process Syst 14: 1057-1064.
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