## 4 Discussion
In July, healthy cotton had a large leaf area and dense canopy, and cotton root rot was also fully pronounced. Dark stalks and dead leaves attached to infested cotton plants greatly changed the canopy morphology and structure of the plants, causing changes in texture features. [PERSON] et al. used image texture to detect diseases of wheat yellow rust and obtained an encouraging result [32]. When using this model to generate prescription maps, OA and PA for the treatment category were 87.14% and 88.10%, respectively. The PA was 8.60% higher than that of the spectral model. This is also due to the ability of texture features for more root rot recognition. However, texture represents the spatial properties of an image region [26], edge effects could cause some healthycotton around the infested areas to be misclassified as infested cotton. As a result, classification commission errors for cotton root rot-infested areas were increased with the texture model. When the spectral-texture model was used to identify cotton root rot, the OA and PA for the infested category in the classification map were 91.87% and 90.80%, respectively. The OA was 1.08% lower than that of the spectral model and the PA was 1.07% higher than that of the spectral model, while OA was 7.06% higher than that of the texture model and the PA was 1.75% lower than of the texture model. The reason for this mixed result may be that the combination of spectral and texture features could increase the ability to identify root rot areas, and at the same time, it could weaken, to a certain extent, the edge effect generated by texture features alone. However, it can be seen from Figure 6 that the blue areas were still more than those of the spectral model, causing the UA and OA to be 4.10% and 1.08%, respectively, lower than those of the spectral model. The OA and PA of the prescription map were 91.40% and 87.72%, respectively, using the spectral-texture model. They were 0.57% and 8.22% higher than the respective values of the spectral model, and the OA was 4.26% higher and the PA was 0.38% lower than the respective values of the texture model. Overall, it can be seen that the addition of texture features had little effect on the OA, but it could improve the ability to identify root rot areas. In a study of a crop classification decision model, [PERSON] et al. (2016) revealed that the contribution of texture features was not decisive compared with multispectral features such as NDVI. In addition, as other studies have reported ([PERSON] et al., 2016) that how texture information provides positive or negative effects on the classification is complex, and how the texture characteristics are affected by object heterogeneity or image spatial resolution need future studies. Thus, the reason that the texture feature did not have a positive effect for the OA as additional information in our study is likely due to the Sentinel-2A spatial resolution. Cotton planting time varies from late March to early April every year in the study area (Edroy, Texas), and the cotton growth and field management practices for different fields differ. Therefore, this work also analyzed the differences in the ability to recognize cotton fields with different infestation levels based on different features of Sentinel-2A images. For cotton fields with relatively severe infestation and more concentrated root rot distributions, such as in Fields 6-8, the spectral model achieved better results. For cotton fields which the infested areas were small and scattered, such as in Fields 1-5, the spectrum-texture model was more appropriate. However, the three models have certain limitations in identifying particularly small root rot zones, which is most obvious in Fields 2 and 3. The reason for this phenomenon is mainly attributed to the spatial resolution of the image. The research of [PERSON] et al. (2018) also proved that the Sentinel-2 sensor could hardly detect root rot-infested areas with a size of less than 100 m\({}^{2}\).