用无人机图像精准识别看麦娘草，减少99%漏喷，节省30%用药面积。
Remote Sensing for Weed Detection and Control
- 筛选600多种模型，平衡精度与推理成本
- 99%以上杂草被精准喷药，用药面积仅比实际大30%
- 适合小面积杂草分布的农田，降本环保
意大利看麦娘草是冬小麦田中常见的竞争性杂草，若不及时控制，会显著降低产量和籽粒品质。为降低除草剂使用成本与环境影响，我们利用无人机和卫星影像进行杂草检测。卫星影像分辨率不足，难以用于精准喷洒，但可用于规划无人机飞行和施药策略；而无人机影像具有足够分辨率，适用于精准喷洒。然而，看麦娘草与作物形态相似，标注需专家知识。我们使用Python分割模型库测试超过600种神经网络架构，评估其在无人机图像中杂草分割的精度与预测成本之间的权衡。最佳系统可对99%以上的杂草实施有效喷药，且喷洒面积仅比人工标注的杂草区域大30%。当杂草仅占田块小部分时，该方法可带来显著经济效益。
原文摘要 · Abstract (English)
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.
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