利用无人机和深度学习准确检测麦田杂草：对生物量和产量影响的研究

摘要：杂草是与农作物竞争光、营养和水的不期望的植物，对农作物生长产生负面影响。准确识别麦田杂草对精确施药 和有针对性地防除杂草具有重要意义。禾本科杂草在其早期生长阶段看起来非常相似的小麦幼苗，使他们难以识别。在 这项研究中，我们专注于不同程度的杂草侵扰的麦田，并使用无人机（UAV）来获取图像。利用深度学习算法和光谱分 析技术，对麦田杂草进行了准确识别和提取。结果表明，该方法对散生麦田杂草的识别准确率为91.27%，对条播麦田杂 草的识别准确率为87.51%。与无杂草区相比，杂草密度的增加导致小麦生物量减少，最大生物量减少了71%。杂草密度 对产量的影响相似，最高减产4320 kg/hm2，减产60%。本研究建立了麦田杂草发生的监测方法，通过准确提取麦田杂草， 研究了不同生育期和杂草密度下杂草对小麦生长的影响。研究结果可为杂草防除和危害评价研究提供参考。

关键词：禾本科杂草；麦田；无人驾驶飞行器；深度学习算法；高光谱成像。

期刊：Plant Methods

DOI：https://doi.org/10.1186/s13007-024-01272-6

设备：GaiaSky

Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications

Abstract：Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.

Keywords：Grass weeds, Wheat field, Unmanned aerial vehicles (UAVs), Deep learning algorithm, Hyperspectral imaging.