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The maintenance of the variability of plant genetic resources is important both for conservation purposes and for the expansion of the genetic bases of the species. Machine learning is an additional tool with great potential that has been used in the search for solving various agricultural challenges. In this article we study the genetic divergence between tobacco genotypes by multivariate methods and verify the efficiency of computational algorithms, aiming at the identification of promising genotypes. Through multivariate analyses, three divergent groups were formed. Both approaches had high accuracy in classification. An artificial neural network with a hidden layer containing three neurons obtained an accuracy of 0.98, while the Random Forest accuracy was 0.98 and the decision tree 0.96. These values demonstrate how powerful these tools are and reinforce their use in studies aimed at maintaining the germplasm bank and also in tobacco conservation and genetic improvement programs in the Recôncavo region of Bahia.

Crop production systems increasingly require sustainable practices such as organic fertilization. Crops like Physalis peruviana L., commonly known as Cape gooseberry, still lack information on the use of organic compost as an alternative to increase productivity. In this scenario, the aim of this study was to evaluate the effect of doses of organic compost on the growth and initial development of P. peruviana L. in a dystrocohesive Yellow Latosol. The experiment was carried out in a greenhouse in a completely randomized design, with six treatments of rates of the organic fertilizer (0, 15, 30, 45 and 60 t ha-1) and an additional control with chemical fertilization. Each treatment consisted of 15 replications. The variables evaluated were plant height, stem diameter, number of leaves, number of flowers, number of fruits, shoot dry mass, and root volume. Cape gooseberry cultivated with organic compost showed growth comparable to those cultivated in soil without fertilization. In this study, the organic fertilizer produced from tree pruning waste and animal manure compost promoted significant gains in height (27.83 cm plant-1), stem diameter (9.23 mm plant-1), number of leaves (51.12 plant-1), number of fruits (5.33 plant-1), shoot dry mass (2.04 g plant-1), and root volume (0.70 g plant-1). The organic compost at the rate 45 t ha-1 provided the largest number of fruits, and therefore, it is recommended for the production of Cape gooseberry in dystrocohese Yellow Latosol.