Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.

Site and time-specific early weed control is able to reduce herbicide use in maize - a case study

Marraccini E;
2021-01-01

Abstract

Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1216342
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