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Abstract
Frost is a natural disaster that can cause catastrophic damages in agriculture, while traditional temperature monitoring in orchards has disadvantages such as being imprecise and laborious, which can lead to inadequate or wasteful frost protection treatments. In this article, we presented a heating requirement assessment methodology for frost protection in an apple orchard utilizing unmanned aerial vehicle (UAV)-based thermal and RGB cameras. A thermal image stitching algorithm using the BRISK feature was developed for creating georeferenced orchard temperature maps, which attained a sub-centimeter map resolution and a stitching speed of 100 thermal images within 30 s. YOLOv4 classifiers for six apple flower bud growth stages in various network sizes were trained based on 5040 RGB images, and the best model achieved a 71.57% mAP for a test dataset consisted of 360 images. A flower bud mapping algorithm was developed to map classifier detection results into dense growth stage maps utilizing RGB image geoinformation. Heating requirement maps were created using artificial flower bud critical temperatures to simulate orchard heating demands during frost events. The results demonstrated the feasibility of the proposed orchard heating requirement determination methodology, which has the potential to be a critical component of an autonomous, precise frost management system in future studies.
Highlights
Extreme weather conditions have always been a major reason for agricultural production loss [1]
We aimed to demonstrate the concept of unmanned aerial vehicle (UAV)-based apple orchard heating requirement determination for frost protection in a high resolution, low time cost heating requirement determination for frost protection in a high resolution, low time cost fashion
In the daytime before a forecasted frost event, orchardists can either fly manually to sample buds at preplanned locations that are representative of the whole orchard, or set up autonomous flight missions to capture bud images as we demonstrated in this article, and generate a flower bud growth stage map from the collected data using a pretrained classifier
Summary
Extreme weather conditions have always been a major reason for agricultural production loss [1]. As one of the agroclimatic risks that is detrimental to crops, frost represents the main cause for weather related damages in agriculture. Due to sudden cold air invasions or the radiative cooling of Earth’s surface, early spring and late fall are the major time periods for frost events [6], and crops can be injured through freeze-induced dehydration and mechanical disruption of cell membranes caused by ice crystals [7,8]. In the United States, frost has caused more economic losses than any other weather-related phenomenon and often devastates local economies [5]
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