No 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.

A disponibilidade de variabilidade genética em bancos de germoplasma é fundamental para o sucesso de programas de melhoramento vegetal. A inferência bayesiana, aliada a técnicas multivariadas, permite identificar essas fontes de variabilidade, auxiliando nas tomadas de decisão no melhoramento de plantas. Logo, objetivou-se avaliar a dissimilaridade genética entre 24 clones de batata-doce por meio de dados qualitativos e quantitativos, utilizando a abordagem multivariada por meio da inferência bayesiana. Na caracterização morfológica, foram utilizados 24 descritores morfológicos e dados de produtividade de raízes e ramas. Para análise estatística foi utilizada a análise multivariada através da inferência bayesiana. Houve variabilidade genética entre os clones de batata-doce. Há grande dissimilaridade dos clones UFVJM05, UFVJM09, UFVJM31, UFVJM37, UFVJM40 com os demais. Esses clones divergentes podem ser utilizados em cruzamentos a fim de obter progênies com alta variabilidade genética. A abordagem multivariada por meio da inferência bayesiana foi eficiente na avaliação da dissimilaridade.

The selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.

The objective was to verify whether convolutional neural networks can help sweet potato phenotyping for qualitative traits. We evaluated 16 families of sweet potato half-sibs in a randomized block design with four replications. We obtained the images at the plant level and used the ExpImage package of the R software to reduce the resolution and individualize one root per image. We grouped them according to their classifications regarding shape, peel color, and damage caused by insects. 600 roots of each class were destined for training the networks, while the rest was used to verify the quality of the fit. We used the python language on the Google Colab platform and the Keras library, considering the VGG-16, Inception-v3, ResNet-50, InceptionResNetV2, and EfficientNetB3 architectures. The InceptionResNetV2 architecture stood out with high accuracy in classifying individuals according to shape, insect damage, and peel color. Image analysis associated with deep learning may help develop applications used by rural producers and improve sweet potatoes, reducing subjectivity, labor, time, and financial resources in phenotyping.