Abstract The corn cropping system can influence the natural enemy identity and the number of Spodoptera frugiperda J.E. Smith, 1797 (Lepidoptera: Noctuidae) individuals infesting the cartridge, ear and stem of this plant. The objectives were to identify the S. frugiperda natural enemies and differences in the number of individuals infesting the cartridge, ear and stem of corn plants under conventional and organic cropping systems, in Brazil after an initial collection of adult males through semiochemical traps. We also evaluated key morphometrical parameters of the larva and factors contributing with the viability of S. frugiperda. A total of 16 and 136 adult males was trapped, and 1,124 and 1,112 larvae was recovered from conventional and organic systems with 4.7 and 6.7% of them parasitized by dipteran and hymenopteran, respectively. Most of the parasitoids recovered had occurrence in both cropping systems, except Cremastinae and Ophion flavidus Brullé, 1846 (Hymenoptera: Ichneumonidae), which were recorded only in organic and Campoletis sp. (Hymenoptera: Ichneumonidae) only in conventional corns. The number of parasitoids recovered was similar in corn plant samples between both cropping systems. A total of 152 and 31 larvae was recovered from corn ears under conventional and organic systems, respectively. Doru luteipes (Scudder, 1876) (Dermaptera: Forficulidae) was recovered from all samples under conventional system. The larva length was overall similar between cropping systems. Parasitism by dipteran and hymenopteran, infection by microorganisms, larva mortality by undetermined causes, and differences in viability of S. frugiperda stages were factors contributing with the supression of this pest. The new associations and parasitoids reported represent possibilities of expanding the biological control strategies to manage S. frugiperda in corn crops.

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.

RESUMO O melhoramento da batata-doce é um trabalho oneroso em decorrência do grande número de características analisadas para a seleção dos melhores genótipos, fazendo-se necessária a adoção de novas tecnologias, como o uso de imagens, associadas ao processo de fenotipagem. Objetivou-se desenvolver uma metodologia para a fenotipagem da produção de raízes para o melhoramento genético de progênies de meios irmãos de batata-doce por meio da análise computacional de imagens e comparar seu desempenho com a metodologia tradicional de avaliação. Foram avaliadas 16 famílias de meios irmãos de batata-doce em delineamento de blocos casualizados com 4 repetições. Avaliou-se a nível de plantas o peso por raiz. As imagens foram adquiridas em um “estúdio” feito com mdf com uma câmera digital modelo Canon PowerShotSX400 IS, sob iluminação artificial. As avaliações foram realizadas no software R, onde para a predição do peso das raízes (em gramas) foi ajustado um modelo de regressão polinomial de segundo grau e foram obtidos os valores genéticos e ganhos esperados. Foi possível predizer o peso das raízes a nível de plantas e de parcelas, obtendo altos coeficientes de determinação entre o peso predito e observado. A visão computacional permitiu a predição do peso das raízes, mantendo o ranqueamento dos genótipos e consequentemente a similaridade entre os ganhos esperados com a seleção. Assim, o uso de imagens é uma ferramenta eficiente para programas de melhoramento genético da batata-doce, auxiliando no processo de fenotipagem da cultura.