Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems

Climate change and cost pressure lead to new environmental and economic challenges that increase the demand for innovative control systems to automate and optimize agricultural tasks. Automating speed control during power-intensive soil tillage can increase eciency and sustainability and counteract...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Kazenwadel, Benjamin
Otros Autores: Geimer, Marcus
Formato: Tesis de maestría
Lenguaje:Español
Publicado: 2024
Materias:
AUTOMATIZACIÓN
OPTIMIZACIÓN
AGRICULTURA
APRENDIZAJE AUTOMÁTICO
MACHINE LEARNING
CONTROL DE VELOCIDAD
REDES NEURALES ARTIFICIALES
Acceso en línea:https://ri.itba.edu.ar/handle/20.500.14769/4283
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-4283
record_format dspace
spelling I32-R138-20.500.14769-42832026-01-15T15:28:38Z Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems Kazenwadel, Benjamin Geimer, Marcus Stein, Alexander Becker, Simon AUTOMATIZACIÓN OPTIMIZACIÓN AGRICULTURA APRENDIZAJE AUTOMÁTICO MACHINE LEARNING CONTROL DE VELOCIDAD REDES NEURALES ARTIFICIALES Climate change and cost pressure lead to new environmental and economic challenges that increase the demand for innovative control systems to automate and optimize agricultural tasks. Automating speed control during power-intensive soil tillage can increase eciency and sustainability and counteract the lack of qualied personnel in agriculture. A survey was carried out focused on tillage by cultivating to obtain an overview of the challenges farmers face during their work, including their target preferences. Based on the obtained requirements for tillage by cultivating, a system was developed automating working depth control by online Lidar plane detection to ensure tillage quality and establish a basis for good plant growth. Automated speed control is realized based on an online-parameterized draft force and traction model combined with the usage of a neural network for fuel rate prediction. The network is trained oine and adaptable to the individual preferences of the farms and varying implements. Thereby, the operator can choose and customize optimization objectives such as fuel eciency, performance, or total cost. During the evaluation, the control system was tested with various objectives against a human driver and was able to perform optimization on the individual objective. Furthermore, the transferability of the system was demonstrated with the usage of another implement. 2024-02-05T18:12:41Z 2024-02-05T18:12:41Z 2021 Tesis de maestría https://ri.itba.edu.ar/handle/20.500.14769/4283 es application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Español
topic AUTOMATIZACIÓN
OPTIMIZACIÓN
AGRICULTURA
APRENDIZAJE AUTOMÁTICO
MACHINE LEARNING
CONTROL DE VELOCIDAD
REDES NEURALES ARTIFICIALES
spellingShingle AUTOMATIZACIÓN
OPTIMIZACIÓN
AGRICULTURA
APRENDIZAJE AUTOMÁTICO
MACHINE LEARNING
CONTROL DE VELOCIDAD
REDES NEURALES ARTIFICIALES
Kazenwadel, Benjamin
Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
topic_facet AUTOMATIZACIÓN
OPTIMIZACIÓN
AGRICULTURA
APRENDIZAJE AUTOMÁTICO
MACHINE LEARNING
CONTROL DE VELOCIDAD
REDES NEURALES ARTIFICIALES
description Climate change and cost pressure lead to new environmental and economic challenges that increase the demand for innovative control systems to automate and optimize agricultural tasks. Automating speed control during power-intensive soil tillage can increase eciency and sustainability and counteract the lack of qualied personnel in agriculture. A survey was carried out focused on tillage by cultivating to obtain an overview of the challenges farmers face during their work, including their target preferences. Based on the obtained requirements for tillage by cultivating, a system was developed automating working depth control by online Lidar plane detection to ensure tillage quality and establish a basis for good plant growth. Automated speed control is realized based on an online-parameterized draft force and traction model combined with the usage of a neural network for fuel rate prediction. The network is trained oine and adaptable to the individual preferences of the farms and varying implements. Thereby, the operator can choose and customize optimization objectives such as fuel eciency, performance, or total cost. During the evaluation, the control system was tested with various objectives against a human driver and was able to perform optimization on the individual objective. Furthermore, the transferability of the system was demonstrated with the usage of another implement.
author2 Geimer, Marcus
author_facet Geimer, Marcus
Kazenwadel, Benjamin
format Tesis de maestría
author Kazenwadel, Benjamin
author_sort Kazenwadel, Benjamin
title Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
title_short Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
title_full Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
title_fullStr Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
title_full_unstemmed Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
title_sort automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
publishDate 2024
url https://ri.itba.edu.ar/handle/20.500.14769/4283
work_keys_str_mv AT kazenwadelbenjamin automationandoptimizationofagriculturalsoiltillageapplyingmachinelearningbasedonmachineandprocesssensorsystems
_version_ 1865139445807710208

Ejemplares similares