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Wyszukujesz frazę "weed interference" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference
Autorzy:
Bagheri, A.
Zargarian, N.
Mondani, F.
Nosratti, I.
Powiązania:
https://bibliotekanauki.pl/articles/2082743.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
prediction models
pulses
weed interference
yield estimation
Opis:
This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) af- fected 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 even- ness 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 stan- dardized 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 net- work approach could be used effectively in lentil yield prediction under weed interference conditions.
Źródło:
Journal of Plant Protection Research; 2020, 60, 3; 284-295
1427-4345
Pojawia się w:
Journal of Plant Protection Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Direct interaction between micronutrients and bell pepper (Capsicum annum L.), to affect fitness of Myzus persicae (Sulzer)
Autorzy:
Alizamani, T.
Shakarami, J.
Mardani-Talaee, M.
Zibaee, A.
Serrao, J.E.
Powiązania:
https://bibliotekanauki.pl/articles/2082745.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
prediction models
pulses
weed interference
yield estimation
Opis:
The green peach aphid, Myzus persicae (Sulzer), is a polyphagous and holocyclic aphid which significantly damages agricultural crops. In the current study, the effects of micro- nutrients on some secondary metabolites of bell pepper (Capsicum annum L.) leaves and their subsequent influence on the life table parameters of M. persicae were investigated under greenhouse conditions. The flavonoid content in bell pepper leaves significantly changed following micronutrient treatments in the wavelength of 270 nm while there were no significant differences in the wavelengths 300 and 330 nm. The highest anthocyanin content was recorded after Fe treatment (3.811 mg ⋅ ml–1) while the total phenolic content in the bell pepper leaves increased after Mn (541.2 mg ⋅ ml –1 ) treatment compared to Fe (254.5 mg ⋅ ml –1 ) and control (216.33 mg ⋅ ml –1 ), respectively. The highest values of intrinsic (r) and finite rates of population increase (λ) of M. persicae were gained with Zn (0.320 and 1.377 day–1 , respectively) treatment although the highest and the lowest values of the mean generation time (T) were found with Fe and Zn (14.07 and 12.63 days, respectively) treat- ments, respectively. Our findings suggest that Mn, more than Zn micronutrients, decreased ecological fitness of green peach aphid and may help enhance the efficiency of pest control techniques.
Źródło:
Journal of Plant Protection Research; 2020, 60, 3; 253-262
1427-4345
Pojawia się w:
Journal of Plant Protection Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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