In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production ($\mathsf{\delta }\mathrm{BMc}$). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r² = 0.99) and BM (r² = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

中文翻译：

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RGB图像衍生指标，用于阔叶杂草对小麦生物量影响的空间评估

在精密农业中，嵌入在自动驾驶汽车中的近端成像系统的开发允许探索针对特定地点的植物应用的新杂草管理策略。要在控制小麦生长的同时对杂草进行准确监测，就需要在生长早期间接测量叶面积指数（LAI）和地上干物质生物量（BM）。本文探讨了RGB图像通过生成两个新指标杂草压力（WP）和当地小麦生物量产量（）评估小麦（Triticum aestivum L.）作物中杂草竞争的潜力。$\mathsf{\delta }\mathrm{骨密度}$）。使用视觉词向量包作为输入，使用SVM-RBF分类器从图像中自动确定农作物和杂草的分数植被覆盖度（FVC）。它基于称为MetaIndex的新植被指数，该指数定义为文献中广泛使用的六种指数的投票。除了简单的杂草侵扰图谱，WP图还描述了作物杂草竞争。同时，δBMc的图评估了当地小麦地上生物量的产量，并告知我们潜在的胁迫。它是由小麦FVC产生的，因为它与LAI（r ² = 0.99）和BM（r ²= 0.93）通过破坏性方法获得。通过结合这两个指标，我们旨在确定小麦胁迫的起源是否是杂草引起的。这种方法为在开发初期使用无损和近端传感技术监测杂草及其在作物生长过程中的竞争提供了新的视角。