Search

Your search keyword '"Danilo Tedesco"' showing total 3 results
Start Over Author "Danilo Tedesco" Topic agriculture (general)
3 results on '"Danilo Tedesco"'

1. CAN BASAL CUTTING BLADE WEAR AFFECT SUGARCANE REGROWTH?

Author

Leonardo Bernache, Danilo Tedesco-Oliveira, Luan P. de Oliveira, Ligia N. Corrêa, and Rouverson P. da Silva

Subjects
Mechanized harvesting, Sugarcane, SPC, Harvester, Agriculture (General), S1-972
Abstract

ABSTRACT Sugarcane harvester cutting blade wear increases ratoon damages and losses, impairing sugarcane regrowth. This study aimed to evaluate the quality of basal cut using ratoon damage and loss indexes and correlating them with effects on sugarcane regrowth. Harvest parameters such as plant height and position, damage and loss indexes, and stem length were evaluated every 30 min, following statistical process control (SPC) assumptions. Blade wear was examined during three harvesting shifts (0-8 h, 8-16 h, and 16-24 h). Sugarcane regrowth was assessed by counting number of tillers and measuring plant height and stem diameter. The second harvesting shift showed better quality in terms of damage and loss indexes, and plant height and stem diameter. Harvesting was considered out of control for parameters such as cutting height, number of tillers, plant height, and stem diameter. On the other side, it was regarded as under control for damage and loss indexes, and plant high and position. The first shift presented peripheral or no damages and weak to medium loss levels, whereas the second and third ones showed extreme damages and medium to strong losses.

Published
2020
Full Text
View/download PDF

2. Potential of using tatistical quality control in agriculture 4.0

Author

Rouverson Pereira da Silva, Adão Felipe dos Santos, Bruno Rocca de Oliveira, Jarlyson Brunno Costa Souza, Danilo Tedesco de Oliveira, and Franciele Morlin Carneiro

Subjects
Statistical process control, Digital agriculture, Quality tools, Quality indicators, Agriculture (General), S1-972
Abstract

ABSTRACT Agriculture 4.0 involves the incorporation of information and communication technologies into machines, equipment, and sensors for use in agricultural production systems. It aims to ease decision-making in agricultural processes. Statistical Quality Control (SQC) is a statistical method with several techniques and tools used to analyze the variability. These tools can be used to provide important information for decision making, including for mechanized agricultural operations. This paper aimed to characterize the worldwide scientific literature on Statistical Process Control use in mechanized agricultural processes, demonstrating its potential to be incorporated into Agriculture 4.0. Our research involved a bibliometric survey on Scopus and Academic Google databases. The analyzed studies allowed us to infer that SQC tools may improve understanding of mechanized operations and be used in Agriculture 4.0. Such features can also streamline and enhance decision making, converting big data into useful information.

Published
2021
Full Text
View/download PDF

3. Potencial de uso do controle estatístico de qualidade na agricultura 4.0

Author

Rouverson Pereira da Silva, Adão Felipe dos Santos, Bruno Rocca de Oliveira, Jarlyson Brunno Costa Souza, Danilo Tedesco de Oliveira, Franciele Morlin Carneiro, Universidade Estadual Paulista (Unesp), Universidade Federal de Viçosa (UFV), and Auburn University

Subjects
Computer science, Agriculture (General), Big data, Scopus, Soil Science, Quality indicators, Scientific literature, Horticulture, Agricultura digital, S1-972, Quality tools, Digital agriculture, Statistical process control, Seven Basic Tools of Quality, Agricultural productivity, Controle Estatístico de Processo, business.industry, Data science, Ferramentas de qualidade, Indicadores de qualidade, ComputingMilieux_GENERAL, Agriculture, Information and Communications Technology, business, Agronomy and Crop Science
Abstract

Agriculture 4.0 involves the incorporation of information and communication technologies into machines, equipment, and sensors for use in agricultural production systems. It aims to ease decision-making in agricultural processes. Statistical Quality Control (SQC) is a statistical method with several techniques and tools used to analyze the variability. These tools can be used to provide important information for decision making, including for mechanized agricultural operations. This paper aimed to characterize the worldwide scientific literature on Statistical Process Control use in mechanized agricultural processes, demonstrating its potential to be incorporated into Agriculture 4.0. Our research involved a bibliometric survey on Scopus and Academic Google databases. The analyzed studies allowed us to infer that SQC tools may improve understanding of mechanized operations and be used in Agriculture 4.0. Such features can also streamline and enhance decision making, converting big data into useful information. RESUMO A Agricultura 4.0 envolve a incorporação de tecnologias de informação e comunicação em máquinas, equipamentos e sensores para uso em sistemas de produção agrícola e tem como principal objetivo, facilitar a tomada de decisões em processos agrícolas. O Controle Estatístico de Qualidade (CEQ) é um método estatístico que utiliza várias técnicas e ferramentas para analisar a variabilidade dos dados, ferramentas estas que podem ser utilizadas para fornecer informações importantes para a tomada de decisões, inclusive para operações agrícolas mecanizadas. Este trabalho teve como objetivo buscar na literatura científica mundial o estado da arte na utilização do Controle Estatístico de Qualidade em processos agrícolas mecanizados, demonstrando também o seu potencial para ser incorporado na Agricultura 4.0. Nossa pesquisa envolveu um levantamento bibliométrico nas bases de dados Scopus e Google Acadêmico. Os estudos analisados permitiram inferir que as ferramentas CEQ podem melhorar a compreensão das operações mecanizadas e serem utilizadas na Agricultura 4.0. Esses recursos também podem agilizar e aprimorar a tomada de decisões, de modo a converter dados massivos (big data) em informações úteis e precisas (right data).

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results