Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3

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 detecti...

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Bibliographic Details
Published in:Applied sciences 2021-03, Vol.11 (5), p.2238
Main Authors: Mekhalfi, Mohamed Lamine, Nicolò, Carlo, Bazi, Yakoub, Al Rahhal, Mohamad Mahmoud, Al Maghayreh, Eslam
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Artificial intelligence
Classification
crop circles
Crops
Datasets
Deep learning
Deserts
Farm management
Farming
Google Earth
instance segmentation
Irrigation
Neural networks
precision farming
Remote sensing
Skin cancer
target detection
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11052238