RESUMO
Saflufenacil is a herbicide recommended for use in the main agricultural crops in Brazil to control eudicotyledonous weeds. However, the application of saflufenacil has been carried out without previous knowledge of this herbicide interactions with soil colloids, which may increase environmental contamination risks. In this study, the leaching of saflufenacil in soil samples was estimated with different organic matter contents. Therefore, PVC columns were filled with samples of Xanthic Ferralsol (XF) and Ferralsol (F), with different physical and chemical attributes. Twelve hours after herbicide (70 g i.a. ha-1) application, the PVC columns were subjected to a rainfall of 60 mm. For the evaluation of saflufenacil leaching, the columns were sectioned in ten parts with 5-cm each. Saflufenacil leached up to 50 and 40 cm deep in the samples, with the lowest contents of organic matter of XF and F, respectively. The increase in organic matter content of Ferralsol and Xanthic Ferralsol samples reduced saflufenacil leaching and the symptoms of intoxication in the indicator species. There was an inverse relation between the content of organic matter and the leaching of the herbicide in both soils studied. It was observed that the application of saflufenacil in soils with low organic matter contents may represent a significant environmental contamination risk of soils and watercourses.
Assuntos
Poluentes do Solo , Percolação , Herbicidas/efeitos adversosRESUMO
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.
Assuntos
Glycine max/fisiologia , Aprendizado de Máquina , Plântula/fisiologia , Sementes/fisiologia , Algoritmos , Inteligência Artificial , Reações Falso-Positivas , Germinação , Processamento de Imagem Assistida por Computador , Análise de Componente Principal , Reprodutibilidade dos Testes , SoftwareRESUMO
ABSTRACT: The application of desiccant herbicides in the bean crop is fundamental in the production of quality seeds, since it anticipates the harvesting season, which makes it possible to obtain seeds in the period of physiological maturation, when they are reported with maximum accumulation of dry matter, high vigor levels and germination percentage. However, there is little information about the ideal application time of these products to obtain beans of high physiological quality. Therefore, the objective of this study was to evaluate the physiological quality of bean seeds after application of desiccant herbicides at different times in preharvest. The experimental design was a randomized complete block design in a 2x3+1 factorial scheme, with four replications. Desiccant herbicides paraquat (400g ha-1) and paraquat + diuron (200 + 400g ha-1) were applied in three phenological stages (R8, R8/R9 and R9), plus one control (without herbicide application). Yield (Kg ha-1), mass of one thousand seeds (g), germination (%) and seed vigor were evaluated through tests of accelerated aging, electrical conductivity and primary root length. Application of paraquat molecule at the R8 stage and the paraquat + diuron mixture at the R8/R9 stage reduced the viability and vigor of the bean seeds, and compromised yield. Applications of the paraquat herbicide at the R8/R9 and R9 stages or of the mixture (paraquat + diuron) at the R9 stage preserved the physiological quality of the seeds without;however, affecting yield.
RESUMO: A aplicação de herbicidas dessecantes na cultura do feijoeiro é fundamental na produção de sementes de qualidade porque antecipa a época de colheita, o que possibilita a obtenção de sementes no período de maturação fisiológica, quando se encontram com máximo acúmulo de matéria seca, elevados níveis de vigor e alta porcentagem de germinação. Todavia, são escassas as informações acerca da época ideal de aplicação desses produtos na obtenção de sementes de feijão de alta qualidade fisiológica. Por essa razão, objetivou-se avaliar a qualidade fisiológica de sementes de feijão após aplicações de herbicidas dessecantes em diferentes épocas na pré-colheita. O delineamento experimental utilizado foi o de blocos casualizados dispostos em esquema fatorial 2x3+1, com quatro repetições. A aplicação dos herbicidas dessecantes paraquat (400g ha-1) e paraquat + diuron (200+400g ha-1) foi realizada em três estádios fenológicos (R8, R8/R9 e R9), mais uma testemunha (sem aplicação do herbicida). Avaliou-se a produtividade (Kg ha-1), a massa de mil sementes (g), a germinação (%) e o vigor das sementes por meio dos testes de envelhecimento acelerado, condutividade elétrica e comprimento da raiz primária. A aplicação da molécula de paraquat no estádio R8 e da mistura paraquat + diuron no estágio R8/R9 reduziu a viabilidade e o vigor das sementes de feijão, além disso comprometeu a produtividade. Aplicações dos herbicidas paraquat nos estádios R8/R9 e R9 ou da mistura (paraquat + diuron) no estádio R9 preservaram a qualidade fisiológica das sementes, sem, entretanto, afetar a produtividade.