Machine learning models as an alternative to determine productivity losses caused by weeds.
Pest Manag Sci
; 77(11): 5072-5085, 2021 Nov.
Article
em En
| MEDLINE
| ID: mdl-34227226
ABSTRACT
BACKGROUND:
Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models have a low generalization capacity to understand different scenarios. However, computational development facilitated the implementation of supervised machine learning models, as artificial neural networks (ANNs), capable of understanding complex relationships. The objectives of our work were to evaluate the ability of ANNs to estimate yield losses in onion (model crop) due to weed interference and compare with multiple linear regression (MLR) and empirical models.RESULTS:
MLR constructed from non-destructive and destructive methods show R2 and root mean square error (RMSE) values varying between 0.75% and 0.82%, 13.0% and 19.0%, respectively, during testing step. The ANNs has higher R2 (higher than 0.95) and lower RMSE (less than 6.95%) compared to MLR and empirical models for training and testing steps. ANNs considering only the coexistence period and system have similar performance to MLR models. However, the insertion of variables related to weed density (non-destructive ANN) or fresh matter (destructive ANN) increases the predictive capacity of the networks to values close to 99% correct.CONCLUSION:
The best performing ANNs can indicate the beginning of weed control since they can accurately estimate losses due to competition. These results encourage future studies implementing ANNs based on computer vision to extract information about the weed community.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Plantas Daninhas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Pest Manag Sci
Assunto da revista:
TOXICOLOGIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Brasil