Direitos para esta edição cedidos à Atena Editora pelos autores. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.
Early yield information of perennial crops is crucial for growers and the industry, which allows cost reduction and benefits crop planning. However, the yield assessment of perennial crops by computational models can be challenging due to diverse aspects of interannual variability that act on the crops. This review aimed to investigate and analyze the literature on yield estimation and forecasting modeling of perennial cropping systems. We reviewed 49 articles and categorized them according to their yield assessment strategy, modeling class used, and input variable characteristics. The strategies of yield assessment were discussed in the context of their principal improvement challenges. According to our investigation, image processing and deep learning models are emerging techniques for yield estimation. On the other hand, machine learning algorithms, such as Artificial Neural Networks and Decision Trees, were applied to yield forecasting with reasonable time in advance of harvest. Emphasis is placed on the lack of representative long-term datasets for developing computational models, which can lead to accurate early yield forecasting of perennial crops.
Direitos para esta edição cedidos à Atena Editora pelos autores. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição 4.0 Internacional (CC BY 4.0). O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. A Atena Editora não se responsabiliza por eventuais mudanças ocorridas nos endereços convencionais ou eletrônicos citados nesta obra. Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação.
Agrochemicals, also known as agrotoxics or pesticides, have been widely used to control the proliferation of pests and weeds in agricultural crops to ensure high planting productivity. Among the most used pesticides in the world is the herbicide glyphosate (N-(phosphonomethyl) glycine), because it proves effective for controlling the annual and perennial growth of weeds in agriculture, forestry, urban areas, and domestic gardens. However, the spraying of this compound on a large scale has caused concern, since it accumulates in the topsoil and can generate negative environmental impacts and damage to human health, as it has toxic potential. Given the above, this study evaluated the phytotoxicity of agrochemical solutions glyphosate in different concentrations for the lettuce species Lactuca sativa Buttercrunch, considering number of germinated seeds, stem length, and root length as parameters of analysis. The concentrations of glyphosate tested were 0.001 mg. L-1; 0.1 mg. L-1; 0.5 mg. L-1; 1 mg. L-1 and 20 mg. L-1. The trial followed the procedures described by the United States Environmental Protection Agency. For the statistical analysis, were considered the number of germinated seeds of each treatment and the length of the roots and stem of each of the germinated seeds, which were inserted into equations for the definition of the percentage parameters of Germination Effect (%GE), Root Growth Inhibition (%RGI) and Germination Index (%GI). Analysis of variance (ANOVA) was performed to test for statistically significant differences among the groups and compared by applying the Tukey Test at the level of 5% significance. The results revealed that there was 62.62% RGI and a GI of only 0.37% in treatment with glyphosate solution with concentration of 20 mg. L-1, indicating that high concentrations of the herbicide have toxic effects for the growth of lettuce species Lactuca sativa Buttercrunch. Only the concentration of 20 mg. L-1 obtained a significant difference in relation to the other concentrations evaluated, including the control treatment for the variable "root growth". Thus, for future work it is recommended that glyphosate solutions with concentrations between 1 and 20 mg. L-1 be evaluated and tested for phytotoxicity, cytotoxicity and genotoxicity.
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