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Abstract
Unmanned aerial vehicle (UAV) images have great potential for various agricultural applications. In particular, UAV systems facilitate timely and precise data collection in agriculture fields at high spatial and temporal resolutions. In this study, we propose an automatic open cotton boll detection algorithm using ultra-fine spatial resolution UAV images. Seed points for a region growing algorithm were generated hierarchically with a random base for computation efficiency. Cotton boll candidates were determined based on the spatial features of each region growing segment. Spectral threshold values that automatically separate cotton bolls from other non-target objects were derived based on input images for adaptive application. Finally, a binary cotton boll classification was performed using the derived threshold values and other morphological filters to reduce noise from the results. The open cotton boll classification results were validated using reference data and the results showed an accuracy higher than 88% in various evaluation measures. Moreover, the UAV-extracted cotton boll area and actual crop yield had a strong positive correlation (0.8). The proposed method leverages UAV characteristics such as high spatial resolution and accessibility by applying automatic and unsupervised procedures using images from a single date. Additionally, this study verified the extraction of target regions of interest from UAV images for direct yield estimation. Cotton yield estimation models had R2 values between 0.63 and 0.65 and RMSE values between 0.47 kg and 0.66 kg per plot grid.
Highlights
In addition to the classification accuracy assessment, open cotton boll detection results were correlated with field-harvested yield data
For the yield estimation analysis, we generated geographical information system (GIS) plot grids that match with field management units and were used to calculate the sum of the open cotton boll area within each grid
The sum of the open cotton boll area was used as an independent variable and field-harvested yield was used as a dependent variable to perform a linear regression analysis
Summary
Advantages of UAV Systems for Agriculture Research. Unmanned aerial vehicle (UAV) technologies have evolved rapidly over the past few years, with great potential for development in agriculture applications. UAV remote sensing technologies enable precise data collection at the field scale with spatial and temporal resolutions that were previously unobtainable using traditional remote sensing platforms. The spatial resolution of UAV data is much finer than the remote sensing data collected from the traditional platforms such as aerial photos and satellite images and can be used in advanced crop analysis for agriculture research applications. UAV data can be acquired with higher frequency when compared to other airborne systems since data collection requires a short preparation time and a small number of people. Data acquisition is possible as flight plans can be scheduled flexibly according to local weather conditions. Cloud-free images can be obtained and flexible sensor configuration and flight route design are possible
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