Datum
2022-03-09Schlagwort
620 Ingenieurwissenschaften 630 Landwirtschaft, Veterinärmedizin Mechanische UnkrautbekämpfungOptimierungGenetischer AlgorithmusMehrkriterielle OptimierungMetadata
Zur Langanzeige
Aufsatz
Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control
Zusammenfassung
Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints.
Zitierform
In: Machines Volume 10 / Issue 3 (2022-03-09) eissn:2075-1702Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202205176191,
author={Hosseini, Hooman and Farzad, Abdulali and Majeed, Faizan and Hensel, Oliver and Nasirahmadi, Abozar},
title={Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control},
journal={Machines},
year={2022}
}
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2022-05-23T15:10:28Z 2022-05-23T15:10:28Z 2022-03-09 doi:10.17170/kobra-202205176191 http://hdl.handle.net/123456789/13852 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ four-bar mechanism goal attainment method hybrid genetic algorithm multi-objective optimization non-dominated sorting genetic algorithm (NSGA-II) quasi-Newton optimization method weed control weed cultivator 620 630 Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control Aufsatz Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints. open access Hosseini, Hooman Farzad, Abdulali Majeed, Faizan Hensel, Oliver Nasirahmadi, Abozar doi:10.3390/machines10030198 Mechanische Unkrautbekämpfung Optimierung Genetischer Algorithmus Mehrkriterielle Optimierung publishedVersion eissn:2075-1702 Issue 3 Machines Volume 10 false 198
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