Remote Sensing for Weed Detection and Control
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917823263014912 |
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| author | Bansal, Ishita Olsen, Peder Estevão, Roberto |
| author_facet | Bansal, Ishita Olsen, Peder Estevão, Roberto |
| contents | Italian ryegrass is a grass weed commonly found in winter wheat fields that are competitive with winter wheat for moisture and nutrients. Ryegrass can cause substantial reductions in yield and grain quality if not properly controlled with the use of herbicides. To control the cost and environmental impact we detect weeds in drone and satellite imagery. Satellite imagery is too coarse to be used for precision spraying, but can aid in planning drone flights and treatments. Drone images on the other hand have sufficiently good resolution for precision spraying. However, ryegrass is hard to distinguish from the crop and annotation requires expert knowledge. We used the Python segmentation models library to test more than 600 different neural network architectures for weed segmentation in drone images and we map accuracy versus the cost of the model prediction for these. Our best system applies herbicides to over 99% of the weeds while only spraying an area 30% larger than the annotated weed area. These models yield large savings if the weed covers a small part of the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Remote Sensing for Weed Detection and Control Bansal, Ishita Olsen, Peder Estevão, Roberto Computer Vision and Pattern Recognition Italian ryegrass is a grass weed commonly found in winter wheat fields that are competitive with winter wheat for moisture and nutrients. Ryegrass can cause substantial reductions in yield and grain quality if not properly controlled with the use of herbicides. To control the cost and environmental impact we detect weeds in drone and satellite imagery. Satellite imagery is too coarse to be used for precision spraying, but can aid in planning drone flights and treatments. Drone images on the other hand have sufficiently good resolution for precision spraying. However, ryegrass is hard to distinguish from the crop and annotation requires expert knowledge. We used the Python segmentation models library to test more than 600 different neural network architectures for weed segmentation in drone images and we map accuracy versus the cost of the model prediction for these. Our best system applies herbicides to over 99% of the weeds while only spraying an area 30% larger than the annotated weed area. These models yield large savings if the weed covers a small part of the field. |
| title | Remote Sensing for Weed Detection and Control |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22554 |