Feasibility of computational vision in the genetic improvement of the production of sweet potato roots

Resumo

The improvement of sweetpotato is a costly job due to the large number of characteristics 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. With that, the objective was to develop a methodology for the phenotyping of the production of roots for the genetic improvement of progenies of half-sib sweetpotato through the computational analysis of images and to compare its performance with the traditional methodology of evaluation. Sixteen half-sib sweetpotato families in DBC 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 with 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. Computer vision allows 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 sweetpotato genetic improvement programs, assisting in the crop phenotyping process.