Rapid evaluation of drought tolerance of winter wheat cultivars under water-deficit conditions using multi-criteria comprehensive evaluation based on UAV multispectral and thermal images and automatic noise removal

Unmanned aerial vehicle (UAV) multispectral and thermal images, combined with machine learning models, have been widely used for high-throughput phenotyping of crop traits and have great potential for evaluating the drought tolerance of winter wheat cultivars. In order to extract the wheat canopy information from UAV images, noise removal is an essential step. Currently, soil and shadow are two of the most common noises in UAV images influencing the extraction of the canopy information, which have been widely studied in previous studies. However, the noise caused by the abnormal canopy temperature in the thermal images has yet to be addressed. Besides, the machine learning-based methods are data-intensive and cannot meet the requirements for rapid evaluation of the drought tolerance of winter wheat cultivars. In order to rapidly evaluate the drought tolerance of winter wheat cultivars, this study proposed a drought tolerance evaluation method for winter wheat cultivars based on multi-criteria comprehensive evaluation and automatic noise removal. The thermal affected zone (TAZ), in which the canopy temperature was abnormally elevated due to thermal radiation from adjacent bare soil, was proposed in this study, and an effective noise removal method was proposed by comparing the accuracy of six automatic image segmentation methods. Canopy vegetation, texture, and temperature indices were extracted from the UAV multispectral and thermal images and selected based on their correlation with the measured yield stability index (YSI). Based on the multiple canopy indices, two multi-criteria comprehensive evaluation methods, i.e., weighted sum based on principal components analysis (PCA-WS) and technique for order preference by similarity to ideal solution based on entropy weight (Entropy-TOPSIS), were used to evaluate the drought tolerance of winter wheat cultivars. The results showed that the automatic image segmentation methods could effectively remove the noises of soil, shadow, and TAZ. Removing the TAZ resulted in a significant decrease in canopy temperature for each irrigation treatment. The total score (TS) and comprehensive evaluation index (CEI) showed a significant linear relationship with the measured YSI, with a maximum R2 of 0.637 and 0.636, respectively. The top five cultivars ranked by the TS and CEI had a consistency ratio of 60–80% with those selected by the measured YSI. This study indicates that the automatic noise removal and multi-criteria comprehensive evaluation have great potential in rapid evaluation of drought tolerance of winter wheat cultivars for large breeding trials.