It is challenging to obtain proper leaf wetting. An angled spray could overcome this impediment, but which spray angle is best suited to droplet size is still unknown. In an outdoor pot experiment, seven doses of cycloxydim and sethoxydim were sprayed with single-orifice standard, anti-drift, and air induction (having a fine, medium, and extremely coarse spray quality, respectively) flat fan nozzles, using spray angles of 10°, 20° backward, 0° (vertical), 10°, 20°, 30°, 40°, 50°, and 60° forward relative to the direction of nozzle trajectory on wild barley at the three-leaf stage. Generally, the forward angled spray was better than the backward angled spray. With a standard flat fan nozzle, the forward angling of spray from 0° to 20° reduced the ED50 from 60.24 to 39.85 g a.i. ⋅ ha−1 for cycloxydim and from 150.51 to 81.13 g a.i. ⋅ ha−1 for sethoxydim. With an anti-drift flat fan nozzle, the forward angling of spray from 0° to 30° reduced the ED50 from 72.57 to 50.20 g a.i. ⋅ ha−1 for cycloxydim and from 181.94 to 104.51 g a.i. ⋅ ha−1 for sethoxydim. With an air induction flat fan nozzle, the forward angling of spray from 0° to 40° reduced the ED50 from 102.96 to 45.52 g a.i. ⋅ ha−1 for cycloxydim and from 209.91 to 92.80 g a.i. ⋅ ha−1 for sethoxydim. More angling did not improve the efficacy of these herbicides. Our results revealed that larger spray droplets needed more spray angle than smaller spray droplets to achieve an equal control.

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.