Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops [Farmer]

Paper Introduction

High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. ^[1]

Figure 1. (A) Armadillo tool carrier equipped with a vision system and six spray nozzles dragging a Hardi ATV trailer with spray liquid; (B) Layout showing all the plots of the trial rotated by 70∘. The colored lines show the overall route plan passing all plots, and the color changes indicate a pit stop every 900 m to refuel the Hardi ATV trailer carrying the herbicide.

References

[1] M. S. Laursen et al. (2016). Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors 2016, 16(11), 1848; https://doi.org/10.3390/s16111848.

Paper Introduction

References

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