Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems.

准确绘制耕地图是进行精确耕作的重要先决条件，因为它有助于田间管理，产量预测和环境管理。作物对种植方式很敏感，有些作物补偿行间间隙的能力有限。使用安装在无人飞行器（UAV）上的传感器进行光学成像是捕获如今覆盖农田的图像的一种经济高效的选择。但是，对此类图像进行视觉检查可能是一项具有挑战性和偏颇的任务，特别是对于一步一步检测植物和行。因此，开发能够同时从UAV图像中分别提取植物和种植行的架构仍然是支持农业系统管理的重要需求。在本文中，我们提出了一种基于卷积神经网络（CNN）的新颖的深度学习方法，该方法可同时检测并定位人工林行，同时考虑到高密度人工林配置对其植物进行计数。在（a）玉米田（（a）柑桔园（Citrus Sinensis Pera）具有不同的生长阶段（即新近种植和成熟的植物）的玉米（Zea mays L. ）。这两个数据集都在不同的位置，不同的农作物类型以及来自不同的传感器和日期来表征不同的植物密度情景。该方案用于证明所提出方法的鲁棒性，从而可以对该方法进行更广泛的讨论。我们的CNN方法实现了两分支架构，其中在种植行中获取的信息被更新到植物检测分支中并进行追溯。-馈送到行分支；然后通过多阶段优化方法进行优化。在玉米种植数据集中（包括生长和成熟两个阶段），我们的方法得出每个图像斑块的平均绝对误差（MAE）为6.224，平均相对误差（MRE）为0.1038，精度和召回值为0.856，和0.905，F值等于0.876。这些结果优于使用相同任务和数据集评估的其他深度网络（HRNet，Faster R-CNN和RetinaNet）的结果。对于人工林行检测，我们的方法分别返回了0.913、0.941和0.925的精度，召回率和F-measure分数。为了测试模型在不同农业类型下的稳健性，我们在柑桔园数据集中执行了相同的任务。它返回的MAE等于每个补丁1.409棵柑橘树，MRE为0.0615，精度为0.922，召回率为0.911，F度量为0.965。对于柑橘种植行的检测，我们的方法得出的精度，召回率和F-measure得分分别等于0.965、0.970和0.964。所提出的方法实现了对来自不同类型农作物的无人机图像中的植物和植物行进行计数和地理定位的最新性能。这里提出的方法可以应用于未来的决策模型，并且可以促进农业系统的可持续管理。所提出的方法实现了对来自不同类型农作物的无人机图像中的植物和植物行进行计数和地理定位的最新性能。这里提出的方法可以应用于未来的决策模型，并且可以促进农业系统的可持续管理。所提出的方法实现了对来自不同类型农作物的无人机图像中的植物和植物行进行计数和地理定位的最新性能。这里提出的方法可以应用于未来的决策模型，并且可以促进农业系统的可持续管理。