Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks

Abstract

Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km 2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task.

Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

“…Figures 6 and 7 present an example of how our modified U-Net algorithm is able to detect and map central pivot irrigation systems of different sizes and land cover, including areas of bare ground, earlier stages, and well-developed crops. Our model was able to successfully segment center pivot irrigation systems of different sizes, with radii between 190 and 950 m, which is a problem for the fixed size sliding window approach proposed by Zhang et al [12]. It was also able to segment pivots that were only partially included in the test area.…”

“…Zhang et al [12] presented an approach based on image classification convolutional neural networks to automatically detect center pivot irrigation systems. The authors experimented with three different architectures: LeNet-5 [13], AlexNet [14], and VGGNet [15].…”

Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection

“…Our automatic approach is, hence, adaptable to search for a wide variety of circular surface structures where digital elevation models (DEMs) are available. Such future studies could focus on common geomorphological features such as meandering rivers, coastal erosion scars, and glacial landforms [10,42,82], but also remote mapping of impact craters from meteors [57], bomb craters [83], and center pivot irrigation [84]. It may be even applicable on extra-terrestrial bodies [85].…”

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

“…On the other hand, regarding irrigation strategies, neural network techniques were applied on satellite images to manage a central pivot irrigation system in Colorado (USA) [17]. The use of several red and red-edge bands for predicting the crop coefficient, Kc, in cotton was studied in [18].…”