Estimation of corn crop damage caused by wildlife in UAV images

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-09-03 DOI:10.1007/s11119-024-10180-7

Przemysław Aszkowski, Marek Kraft, Pawel Drapikowski, Dominik Pieczyński

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引用次数: 0

Abstract

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.

Abstract Image

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估算无人机图像中野生动物对玉米作物造成的损害

目的本文提出了一种低成本、低功耗的解决方案，利用无人飞行器（UAV）采集的田间图像，确定野生动物设施损坏的玉米作物面积。方法该方法利用基于深度卷积神经网络（如 UNet 系列）和变换器（SegFormer）的图像分割模型，这些模型在波兰西部超过 300 公顷的不同玉米田中经过训练。测试结果表明，尽管该方法仅使用了廉价消费级无人机上易于获取的 RGB 数据，但其准确性足以应用于农业相关任务的实际解决方案中，因为用于分割健康和受损作物的 IoU（Intersection over Union）指标达到了 0.88。处理代码和训练有素的模型已公开共享。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.