Paper record
Feasibility of computational vision in the genetic improvement of sweet potato root production
Horticultura Brasileira · 1 Dec 2022 · 10.1590/s0102-0536-20220405
Abstract
ABSTRACT The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process.
Code and data availability
The article describes image-based phenotyping of sweet potato roots (2233 root images, R/ExpImage analysis) but contains no data availability statement, no public dataset or image bank deposit, and no author code repository URL. The only URLs present are the article DOI and a cited reference (Haque et al. 2021), which是
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