Controlling weed infestation through chemicals (herbicides & pesticides) is essential for crop yield. However, excessive use of these chemicals has caused severe agronomic and environmental problems. According to accurate weed detection, an appropriate dose of herbicides is recommended in site-specific weed management (SSWM) applications, which may ultimately promote chemical saving while enhancing its effectiveness. In this context of SSWM, an accurate detection and recognition system needs to be established for recognizing weeds and crops to carry out precise agrochemical treatments in real-time applications. Unmanned aerial vehicles (UAVs) and other robots offer potential in precision agriculture (PA) applications by monitoring farmland on a per-plant basis, as they have the capability of acquiring high-resolution imagery providing detailed information for the distribution of crops and weeds in the field. In this regard, UAVs offer a cost-efficient solution for providing excellent survey capabilities. In this study, a deep learning system is developed for identifying weeds and crops in croplands. The developed system was implemented and evaluated using high-resolution UAV imagery captured over two different target fields: pea and strawberry; the developed system was able to identify weeds with an average accuracy of 95.3%, whereas the overall average accuracy (crops and weeds) was 94.73% for both the fields. The average kappa coefficient of the developed system was 0.89. The developed deep learning system outperformed the existing machine learning and deep learning-based approaches on comparison and can be embedded into a precision sprayer for adopting the SSWM strategy.

通过化学物质（除草剂和杀虫剂）控制杂草的侵害对作物的产量至关重要。但是，过度使用这些化学物质已引起严重的农艺和环境问题。根据精确的杂草检测，在特定地点的杂草管理（SSWM）应用中建议使用适当剂量的除草剂，这最终可以促进化学物质的节省，同时提高其有效性。在SSWM的背景下，需要建立一个准确的检测和识别系统来识别杂草和农作物，以便在实时应用中进行精确的农用化学处理。无人驾驶飞机（UAV）和其他机器人通过逐工厂监控农田，为精密农业（PA）应用提供了潜力，因为他们具有获取高分辨率图像的能力，可提供有关田间作物和杂草分布的详细信息。在这方面，无人机提供了具有成本效益的解决方案，以提供出色的勘测功能。在这项研究中，开发了一种用于识别农田中的杂草和农作物的深度学习系统。所开发的系统是通过在两个不同的目标区域（豌豆和草莓）上捕获的高分辨率无人机图像实施和评估的。开发的系统能够识别杂草，平均准确度为95.3％，而两个领域的总体平均准确度（作物和杂草）为94.73％。所开发系统的平均卡伯系数为0.89。