Uncooled thermal cameras have been employed as common UAV payloads for aerial temperature surveillance in recent years. Due to the lack of internal cooling systems, such cameras often suffer from thermal-drift-induced nonuniformity or vignetting despite having built-in mechanisms to minimize the noise. The current study examined a UAV-based uncooled thermal camera vignetting regarding camera warmup time, ambient temperature, and wind speed and direction, and proposed a simple calibration-based vignetting migration method. The experiments suggested that the camera needed to undergo a warmup period to achieve stabilized performance. The required warmup duration ranged from 20 to 40 min depending on ambient temperature. Camera vignetting severity increased with camera warmup time, decreasing ambient temperature, and wind presence, while wind speed and direction did not make a difference to camera vignetting during the experiments. Utilizing a single image of a customized calibration target, we were able to mitigate vignetting of outdoor images captured in a 30 min duration by approximately 70% to 80% in terms of the intra-image pixel standard deviation (IISD) and 75% in terms of the pixel-wise mean (PWMN) range. The results indicated that outdoor environmental conditions such as air temperature and wind speed during short UAV flights might only minimally influence the thermal camera vignetting severity and pattern. Nonetheless, frequent external shutter-based corrections and considering the camera nonlinear temperature response in future studies have the potential to further improve vignetting correction efficacy for large scene temperature ranges.

Thinning is an important routine for apple growers to manage crop load and improve fruit quality, which can be accomplished through manual, chemical, or mechanical manipulation of flowers and fruitlets. Traditionally, blossom thinning relies on human experts’ visual evaluation of the flower load, a leading indicator of crop load, which can be imprecise and prone to errors. This study aimed to develop an apple blossom density mapping algorithm utilizing point clouds reconstructed through unmanned aerial vehicle (UAV)-based red-green-blue (RGB) imagery and photogrammetry. The algorithm was based on grid average downsampling and white color thresholding, and it was able to generate top-view blossom density maps of user-defined tree height regions. A preliminary field experiment was carried out to evaluate the algorithm’s accuracy using manual blossom counts of apple tree row sections as ground truths, and a coefficient of determination (R²) of 0.85, a root mean square error (RMSE) of 1307, and a normalized RMSE (NRMSE) of 9.02% were achieved. The algorithm was utilized to monitor the blooming of the apple tree rows and was demonstrated to effectively show blossom density variations between different tree rows and dates. The study results suggested the potential of UAVs as a convenient tool to assist precise blossom thinning in apple orchards, while future research should further investigate the reliability of photogrammetry techniques under different image qualities and flight settings as well as the influence of blossom distribution on algorithm accuracy.