This dataset consists of approximately 13,000 jpg format images. These images were collected using consumer grade trail cameras manufactured by Browning Trail Cameras. Cameras were installed across Mississippi (USA) in 2019 and 2020 from March through September. Images collected are exclusively oblique, unobstructed views of the sky. Cameras were placed in time-lapse mode and set to collect one image every hour. Our intent in this work was to first compare deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. Radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Using this dataset, we then developed an artificial-intelligence-based edge computing system to fully automate the classification process.
College of Agriculture and Life Sciences
Department of Plant and Soil Sciences| Mississippi Agricultural and Forestry Experiment Station
Geosystems Research Institute
Mississippi, precision agriculture, clouds, weather, remote sensing, radiometric quality, unmanned aerial systems
Agronomy and Crop Sciences
Czarnecki, Joby; Samiappan, Sathishkumar; Wasson, Louis; and McCraine, C. Daniel, "Mississippi Sky Conditions" (2021). College of Agriculture & Life Sciences Publications and Scholarship. 27.