Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference
This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2020-08-01
|
| Series: | Journal of Plant Protection Research |
| Subjects: | |
| Online Access: | https://doi.org/10.24425/jppr.2020.133953 |
| _version_ | 1818870557387522048 |
|---|---|
| author | Alireza Bagheri Negin Zargarian Farzad Mondani Iraj Nosratti |
| author_facet | Alireza Bagheri Negin Zargarian Farzad Mondani Iraj Nosratti |
| author_sort | Alireza Bagheri |
| collection | DOAJ |
| description | This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected
by weeds using artificial neural network and multiple regression models. Systematic
sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil
maturity. The weed density and height as well as canopy cover of the weeds and lentil were
measured in the first sampling stage. In addition, weed species richness, diversity and evenness
were calculated. The measured variables in the first sampling stage were considered
as predictive variables. In the second sampling stage, lentil yield and biomass dry weight
were recorded at the same sampling points as the first sampling stage. The lentil yield and
biomass were considered as dependent variables. The model input data included the total
raw and standardized variables of the first sampling stage, as well as the raw and standardized
variables with a significant relationship to the lentil yield and biomass extracted
from stepwise regression and correlation methods. The results showed that neural network
prediction accuracy was significantly more than multiple regression. The best network in
predicting yield of lentil was the principal component analysis network (PCA), made from
total standardized data, with a correlation coefficient of 80% and normalized root mean
square error of 5.85%. These values in the best network (a PCA neural network made from
standardized data with significant relationship to lentil biomass) were 79% and 11.36% for
lentil biomass prediction, respectively. Our results generally showed that the neural network
approach could be used effectively in lentil yield prediction under weed interference
conditions. |
| first_indexed | 2024-12-19T12:08:55Z |
| format | Article |
| id | doaj.art-a1766507179d4300b404b64feeeb20b0 |
| institution | Directory Open Access Journal |
| issn | 1899-007X 1899-007X |
| language | English |
| last_indexed | 2024-12-19T12:08:55Z |
| publishDate | 2020-08-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Journal of Plant Protection Research |
| spelling | doaj.art-a1766507179d4300b404b64feeeb20b02022-12-21T20:22:16ZengPolish Academy of SciencesJournal of Plant Protection Research1899-007X1899-007X2020-08-0160328429510.24425/jppr.2020.133953Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interferenceAlireza Bagheri0https://orcid.org/0000-0003-0870-649XNegin Zargarian1Farzad Mondani2Iraj Nosratti3Department of Agronomy and Plant Breeding, Razi University, Kermanshah, IranDepartment of Agronomy and Plant Breeding, Razi University, Kermanshah, IranDepartment of Agronomy and Plant Breeding, Razi University, Kermanshah, IranDepartment of Agronomy and Plant Breeding, Razi University, Kermanshah, IranThis study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.https://doi.org/10.24425/jppr.2020.133953neural networkprediction modelspulsesweed interferenceyield estimation |
| spellingShingle | Alireza Bagheri Negin Zargarian Farzad Mondani Iraj Nosratti Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference Journal of Plant Protection Research neural network prediction models pulses weed interference yield estimation |
| title | Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference |
| title_full | Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference |
| title_fullStr | Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference |
| title_full_unstemmed | Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference |
| title_short | Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference |
| title_sort | artificial neural network potential in yield prediction of lentil lens culinaris l influenced by weed interference |
| topic | neural network prediction models pulses weed interference yield estimation |
| url | https://doi.org/10.24425/jppr.2020.133953 |
| work_keys_str_mv | AT alirezabagheri artificialneuralnetworkpotentialinyieldpredictionoflentillensculinarislinfluencedbyweedinterference AT neginzargarian artificialneuralnetworkpotentialinyieldpredictionoflentillensculinarislinfluencedbyweedinterference AT farzadmondani artificialneuralnetworkpotentialinyieldpredictionoflentillensculinarislinfluencedbyweedinterference AT irajnosratti artificialneuralnetworkpotentialinyieldpredictionoflentillensculinarislinfluencedbyweedinterference |