The Acrocomia aculeata is one of the most promising plants for sustainable production of renewable energy. In order to understand patterns of the distribution of the allelic diversity of A. aculeata ex situ germplasm collection, the present study investigated the hypothesis that the genetic variability of the accessions may match their geographical origin. A genotypic analysis of 77 A. aculeata accessions was conducted with 6 simple sequence repeat markers. A high degree of molecular diversity among the accessions was found, with an average of 9 alleles per locus and a polymorphic information content with a mean of 0.76. A total of 4 clusters was identified by the Bayesian analysis of population structure. The highest subpopulation diversity was identified in Pop1, mainly formed by accessions from State of Mato Grosso do Sul. The populations Pop2A, Pop2B, and Pop2C, all from the State of Minas Gerais, showed high genetic variability as determined by a higher F_st, and a wide genetic variance, which were identified within and among the population by analysis of molecular variance. Based on our results and on Vavilov’s theory on crop origins, one possible diversity center for A. aculeata is proposed to be in a region in southeast Brazil.

Grafting using suitable rootstocks mitigates the adverse effects caused by environmental stresses such as water deficit in the tomato crop. Solanum pennellii and Solanum peruvianum, the wild relatives of tomato, are used as rootstocks due to their tolerance to water deficit and soil-borne diseases. This study focused on evaluating physiological and biochemical responses of tomato plants grafted onto S. pennellii and S. peruvianum rootstocks during water deficit. The commercial tomato cultivar ‘HM 1823’ (HM) either self-grafted (HM/HM) or grafted onto S. pennellii (HM/PN), S. peruvianum (HM/PR), and ‘Multifort’ (HM/MU) rootstocks were subjected to water-deficit stress by withholding irrigation for eight days. The performance of the grafted plants under water deficit was evaluated using physiological and biochemical parameters in vegetative tissues of the grafted plants. Plants grafted using S. pennellii (PN) and S. peruvianum (PR) rootstocks showed higher values of water potential (Ψw), relative water content (RWC), net photosynthetic rate (A), and leaf water use efficiencies (WUE) compared to HM, HM/HM, and HM/MU. Plants grafted onto tomato wild relatives showed the lowest malondialdehyde (MDA) and proline content. This study demonstrated that the rootstocks of wild tomato relatives reduced the effect of water deficit to a greater extent through better physiological, metabolic, and biochemical adjustments than self-grafting plants.

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

The selection of late blight resistant genotypes of tomato requires many evaluations on the field. The use of machine learning models to assess late blight severity based on images from multispectral cameras onboard of unmanned aerial vehicles (UAVs) can bring efficiency and quickly in evaluations. In this work, we use remote sensing and machine learning techniques to assess late blight resistance of tomato lines grown in open field conditions. Seventy-six tomato lines, including two resistant lines and one susceptible line, were used to quantify late blight severity. Plants were arranged according to a replicated check design in a total of 132 experimental plots. Tomato plants were artificially inoculated with a Phytophthora infestans zoospore suspension. Multispectral images were obtained using an unmanned aerial vehicle. We calculated vegetation indexes (VI) using the images, which were the basis for building the Random Forest models used to predict disease severity. Two methodologies were used to predict late blight severity: Methodology 1, which used only the images from the last day of evaluation, and Methodology 2, which used the images from four days of evaluation. For Methodology 1 and 2, determination coefficients of 0.81 and 0.93 were obtained for the test set, respectively. Methodology 2 was used to predict late blight severity of the 132 field plots. Tomato plots were sorted from lowest to highest predicted severity. Resistant plots were ranked first indicating consistency of prediction. We therefore recommend Methodology 2 as a fast and practical way to predicted late blight severity in breeding populations.