Purpose

This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.

Methods

The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.

Results

The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.

Conclusion

The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

中文翻译：

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无人机图像中野生动物对玉米作物造成的损害估计

 目的

本文提出了一种低成本、省力的解决方案，利用无人机（UAV）收集的现场图像来确定野生动物设施受损的玉米作物面积。所提出的解决方案可以确定受损作物的百分比及其位置。

 方法

该方法利用基于深度卷积神经网络（例如 UNet 系列）和变压器（SegFormer）的图像分割模型，这些模型在波兰西部 300 多公顷的不同玉米田上进行了训练。对一系列神经网络架构进行了测试，以选择最准确的最终解决方案。

 结果

测试表明，尽管仅使用廉价消费级无人机提供的易于访问的 RGB 数据，该方法仍达到了足够的精度，可应用于农业相关任务的实际解决方案，如用于分割健康的 IoU（交集）指标作物受损程度达到0.88。

 结论

所提出的方法可以轻松计算玉米作物损失的总百分比和可视化。处理代码和训练模型是公开共享的。