Integração de métodos multicritério na busca da sustentabilidade agrícola para a produção de tomates no município de São José de Ubá-RJ.

Este estudo discute alternativas para os sistemas produtivos da terra no plantio de tomates em São José de Ubá-RJ e as relações de trabalho passíveis de serem colocadas em pratica de forma a promover uma agricultura sustentavel na região, priorizando os aspectos economicos, ambientais e culturais do problema. A introdução da questão cultural aumenta a complexidade da questão, sendo necessário o uso de uma metodologia capaz de lidar com toda a subjetividade envolvida nesse processo de tomada de decisão. O estudo visa contribuir para a conquista de um processo sustentavel na região, estabelecendo um processo de tomada de decisão pautado nas opinioes do agricultor, respeitando principalmente as questões culturais do problema. As características do ambiente de tomada de decisão indicaram o uso de uma combinação de métodos de apoio a decisão multicritério - MACBETH e VIP Analysis - para a seleção da melhor alternativa capaz de possibilitar o alcance dos objetivos propostos

Machine learning approaches for large scale classification of produce

Abstract The analysis and identification of different attributes of produce such as taxonomy, vendor, and organic nature is vital to verifying product authenticity in a distribution network. Though a variety of analysis techniques have been studied in the past, we present a novel data-centric approach to classifying produce attributes. We employed visible and near infrared (NIR) spectroscopy on over 75,000 samples across several fruit and vegetable varieties. This yielded 0.90–0.98 and 0.98–0.99 classification accuracies for taxonomy and farmer classes, respectively. The most significant factors in the visible spectrum were variations in the produce color due to chlorophyll and anthocyanins. In the infrared spectrum, we observed that the varying water and sugar content levels were critical to obtaining high classification accuracies. High quality spectral data along with an optimal tuning of hyperparameters in the support vector machine (SVM) was also key to achieving high classification accuracies. In addition to demonstrating exceptional accuracies on test data, we explored insights behind the classifications, and identified the highest performing approaches using cross validation. We presented data collection guidelines, experimental design parameters, and machine learning optimization parameters for the replication of studies involving large sample sizes

The first cytogenetic data on Strumigenys louisianae Roger, 1863 (Formicidae: Myrmicinae: Dacetini): the lowest chromosome number in the Hymenoptera of the neotropical region.

In the present study, the first cytogenetic data was obtained for the ant species Strumigenys louisianae, from a genus possessing no previous cytogenetic data for the Neotropical region. The chromosome number observed was 2n = 4, all possessing metacentric morphology. Blocks rich in GC base pairs were observed in the interstitial region of the short arm of the largest chromosome pair, which may indicate that this region corresponds to the NORs. The referred species presented the lowest chromosome number observed for the subfamily Myrmicinae and for the Hymenoptera found in the Neotropical region. Observation of a low chromosome number karyotype has been described in Myrmecia croslandi, in which the occurrence of tandem fusions accounts for the most probable rearrangement for its formation. The accumulation of cytogenetic data may carry crucial information to ensure deeper understanding of the systematics of the tribe Dacetini