Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 19; p. 3859
• Main Authors: Czarnecki, Joby M. Prince, Samiappan, Sathishkumar, Zhou, Meilun, McCraine, Cary Daniel, Wasson, Louis L.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2021
• Subjects: Accuracy; Agricultural land; Agricultural production; Agriculture; Artificial intelligence; Artificial neural networks; Automation; autonomous systems; Classification; Classifiers; Cloud computing; cloud detection; Clouds; Computers; Decision making; Deep learning; Edge computing; Farming systems; Image classification; Image degradation; Image quality; Low altitude; low-altitude remote sensing; Machine learning; Management decisions; Neural networks; Precision farming; Quality control; Remote sensing; ResNet; Shadows; UAS image quality; Visible spectrum
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13193859

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.