Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches

Author:

Felipe Maldaner Leonardo,de Paula Corrêdo Lucas,Fernanda Canata Tatiana,Paulo Molin José

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference52 articles.

1. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption;Ahmad;Energy Build.,2017

2. Design and validation of an electronic data logging system (CAN Bus) for monitoring machinery performance and management- Planting application. American Society of Agricultural and Biological Engineers Annual International Meeting 2018;Al-Aani;ASABE,2018

3. Assessing extraction trail trafficability using harvester CAN-bus data;Ala-Ilomäki;Int. J. Forest Eng.,2020

4. Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems;Blasch;Precis. Agric.,2020

5. Sugar Cane Yield Monitoring System;Benjamin;Appl. Eng. Agric.,2001

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