Abstract
Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.
Highlights
The Southern Amazon agricultural frontier has long been studied for its rapid expansion associated with high deforestation rates [1,2]
Recent studies point out that the climate change implications for the Southern Amazon may result in a shorter rainy season, which may prevent the adoption of double cropping systems on the long run [9,10]
The proposed method consists of three parts: in the first part, PVANET was used to detect the center pivot irrigation systems candidates; in the second part, GoogLeNet [37]
Summary
The Southern Amazon agricultural frontier has long been studied for its rapid expansion associated with high deforestation rates [1,2]. The last decade has been marked by the adoption of intensive agricultural practices often considered to be an efficient strategy to ensure high agricultural productivity and diversification while limiting deforestation [3]. In this regard, a widely studied intensive practice is double cropping, which consists of cultivating two crops (usually soybean followed by maize or cotton) sequentially in a same year [4,5,6]. Recent studies point out that the climate change implications for the Southern Amazon may result in a shorter rainy season, which may prevent the adoption of double cropping systems on the long run [9,10]. Mapping central pivot irrigation systems appears essential to (1) monitor the evolution of the Southern
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
