International Journal of Plant Breeding and Genetics1819-35952152-3347Academic Journals Inc.10.3923/ijpbg.2017.19.24DanielI.O. AtinsolaK.O. AjalaM.O. PopoolaA.R. 12017111Background and Objective: High precision phenotype data improve efficiency during selections within breeding populations. The objectives of this study were to evaluate precision of plant phenotyping methods within a tomato breeding population assembled at the University of Agriculture, Abeokuta, Nigeria. The precision of three phenotyping methods namely field evaluation of morphological characteristics (FM), laboratory digital imaging analysis of fruits (FDI) and seeds (SDI) were tested on 10 tomato accessions within the breeding population. Materials and methods: The FM phenotyping involved a randomized complete block design field experiment with three replications to get data on shoot length, branch number, leaf number, node number, flower number and fruit number. The FDI phenotyping was carried out on randomly selected fruits from each of the replicates with the aid of Veho™ software to obtain digital data on fruit length, fruit width, fruit radius, fruit circumference and fruit surface area. The SDI phenotyping was done on three replicates of seeds of each tomato accession, scanned with the Winseedle™ equipment which estimates seed length, curve length, seed width, curve width, curvature, volume circle, surface area and width-length ratio. Principal Component Analysis (PCA) was done on the data sets. Results: In PCA 2, cumulative eigen values were 68.02% for FM descriptors, 96.17% for FDI descriptors and 84.45% for SDI descriptors, indicating that digital imaging data of fruit descriptors would best distinguish this tomato breeding population. Among the FDI descriptors, fruit width and associated traits like fruit surface area, radius and circumference had the highest eigen vector loading of the PCA (0.43) and so was adjudged the best distinguishing trait. Conclusion: It was concluded that digitalized phenotyping offer more precision than manual phenotyping of the tomato breeding population. Laboratory digitalized fruit descriptors constitute the most precise phenotyping dataset and thus recommended for rapid discrimination and parental selections within the population.]]>FAOSTAT.,20142014Fufa, F., P. Hanson, S. Dagnoko and M. Dhaliwal,20119118798Foolad, M.R. and D.R. Panthee,20123193123Inoue-Nagata, A.K., M.F. Lima and R.L. Gilbertson,201634818Poland, J.,20142014Araus, J.L. and J.E. Cairns,2014195261Sokefeld, M., R. Gerhards, W. Kuhbauch and A. Nabout,199952183191Granitto, P.M., P.A. Garralda, P.F. Verdes and H.A. Ceccatto,200333439Varma, V.S., K.K. Durga and K. Keshavulu,200313036Tetsuka, T. and A. Uchino,2005Fagopyrum esculentum Moench).]]>86064Venora, G., O. Grillo, M.A. Shahin and S.J. Symons,200740161166Poland, J.A. and R. Nelson,2011101290298Daniel, I.O.,201234183192Daniel, I.O., K.A. Adeboye, O.O. Oduwaye and J. Porbeni,20126245251Xie, W., K. Yu, K.P. Pauls and A. Navabi,2012102434442Wuerschum, T.,20152015MacRae, I.,20162016Popoola, A.R., P.D. Kaledzi, D.K. Ojo, D.A. Adegbite and Y. Falana et al.,20122012Popoola, A.R., M.R. Ercolano, P.D. Kaledzi, F. Ferriello and S.A. Ganiyu et al.,2012Fusarium wilt.]]>106167SAS.,2009Vidavski, F., H. Czosnek, S. Gazit, D. Levy and M. Lapidot,2008Tomato yellow leaf curl virus from different wild tomato species.]]>127625631Johnson, D.E.,1998Pages: 360Pages: 360Raji, A.A.,2002Manihot esculentus Crantz) germplasm.]]>2002