# Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
###### Abstract  
Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. The dataset totals 2511 crop circle samples. With a small training set and a relatively large test set, plausible detection rates were obtained, scoring a precision of 1 and a recall of about 0.82 for Mask R-CNN and a precision of 0.88 and a recall of 0.94 regarding YOLOv3. crop circles; target detection; instance segmentation; Google Earth; deep learning; precision farming; Mask R-CNN; YOLOv3 +
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patch-to-patch mapping was implemented within a deep learning architecture for remote sensing image registration in [10]. In [11], ternary change detection in Synthetic Aperture Radar data is addressed, where an autoencoder was used to learn meaningful features, followed by a three-group clustering. The resulting features are fed into a CNN for change classification. Owing to viewpoint, rotation and scale changes on the one hand and the lack of abundant data (i.e., often manual annotation may be somewhat laborious) on the other, object detection in remote sensing images remains harder to approach than the typical scene classification. However, as per object detection, the literature suggests fewer contributions. On this point, the work in [12] combines a multiscale object proposal network with an accurate object detection network by fusing feature maps, and satisfactory results were yielded. In order to tackle the rotation invariance issue that may be encountered with CNNs, in [13], a rotation-invariant layer was trained prior to fine-tuning the CNN model. This enables drawing the training samples close to each other before and after rotation. To cope with scale change, a scale-adaptive architecture was presented in [14], where multilayer region proposal networks are envisioned, topped with a fusion network for object detection. Another rotation-invariant network was devised in [15], where multiangle anchors are incorporated into the region proposal network. Further, a fusion network was suggested to learn contextual cues. In [16], a saliency mechanism was implemented within a deep belief network to located potential objects coarsely within a probe image. This coarse object proposal has demonstrated its ability to lessen exhaustive search for objects. In this respect, deep learning has also found its way into other applications such as precision farming. In [17], transfer learning (based on a VGG-16 pretrained network) was applied to identify mildew disease in a pearl millet. In [18], summer crop classification was addressed based on Landsat Enhanced Vegetation Index by means of a long short-term memory network and uni-dimensional CNN, among other state-of-the-art classifiers. It turned out that 1D-CNN performs the best. A comparative study of three pre-trained deep learning models was carried out in [19] for water stress assessment in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max). Specifically, AlexNet, GoogLeNet and Inception V3 were confronted on a 1200 optical image dataset. Thus, GoogLeNet stands out among the three models. In [20], AlexNet and GoogLeNet were evaluated on a dataset amounting to 54,306 images pertaining to 14 crop species and 26 plant diseases. Although both of the networks perform fairly equally, GoogLeNet yields slightly higher scores under a transfer learning scenario.