# Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
###### Abstract
However, the score gap increases when the networks are trained from scratch. In [21], a digital surface model was combined with a radiometric index image and fed as input to a CNN for soil and crop segmentation in remote sensing data. Crop yield estimation/counting via artificial intelligence holds a great interest in precision agriculture. For instance, it is useful for the farmers in order to allocate essential logistics such as transportation means and labor force. This implies that overestimating the yield may raise management costs, whereas in the case of underestimation, crops may be subject to waste if not transferred to storage facilities shortly after the harvest. In [22], a computer vision system was devised for kiwifruit yield estimation. The system incorporates a lightweight optical sensor mounted on a tractor that drives along the kiwi plants for image acquisition. Regarding the fruit detection part, a cascaded Haar feature-based classifier is adopted. In [23], cotton yield estimation is approached by considering several features fed into an Artificial Neural Network (ANN) as input, namely multi-temporal features (i.e., including canopy cover, canopy height, canopy volume, normalized difference vegetation index, excessive greenness index) and non-temporal features (i.e., cotton bolt count, bolt size and bolt volume), supplemented with irrigation status. Another ANN that takes as input multi-temporal features (e.g., canopy cover) and weather information for tomato yield estimation was presented in [24]. A review on machine learning in agriculture is given in [25; 26; 27; 28; 29; 30; 31]. Crop circles have been evolving as a large-scale farming scheme, especially in countries consisting mostly of desert. Crop circles are normally planned around areas in the desert with a large body of underground water reserves. Their circular shape is owed to the irrigation practice, commonly referred to as pivot irrigation, mainly adopted for optimizing water use. Automatic crop circle detection is necessary for a precise, low-cost and timely management of farming land in remote areas in the desert. In this context, this article investigates the use of deep learning for crop circle detection in the desert. We selected Mask R-CNN and YOLOv3 models, owing to their performance in various applications. Mask R-CNN offers the possibility to carry out instance segmentation of the target and has demonstrated plausible performance in several applications. For instance, in [32], U-Net and Mask R-CNN were combined for nuclei segmentation in fluorescence and histology images, where a precision of 0.72 and a recall of 0.6 were obtained. In [33], Mask R-CNN was adopted for strawberry detection, and a precision of 0.96 and a recall of 0.95 were achieved. In [34], vehicle damage detection was addressed by means of Mask R-CNN with a detection accuracy of 94.53%. Knee meniscus tear detection was carried out via Mask R-CNN in [35], scoring a weighted AUC score of 0.906. In [36], melanoma skin cancer detection was tackled by combining Mask R-CNN for initial segmentation, followed by DenseNet for feature extraction, topped by least square support vector machines to draw the final result, where the accuracy ranged between 88.5% and 96.3%. YOLOv3 has been widely considered for object detection. For instance, in [37], it was compared with Faster R-CNN for car detection in unmanned aerial vehicle images. It turned out that YOLOv3 is more accurate than R-CNN, with a precision and recall of 0.99 and 0.99, respectively. Apple lesion detection was addressed by means of a YOLOv3-based architecture with an accuracy of 95.57% in [38]. Apple detection was also achieved based on YOLOv3-dense, with an F1 score of 0.817 [39]. YOLOv3 was applied in [40] for traffic flow detection, with an accuracy of 95.01%. YOLOv3 was adopted for collapsed building detection in [41] with a precision of 0.88 and a recall of 0.78.