*Article* **A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing**

**Tianyi Wang 1,\*, J. Alex Thomasson 1,2, Thomas Isakeit <sup>3</sup> , Chenghai Yang <sup>4</sup> and Robert L. Nichols <sup>5</sup>**


Received: 25 June 2020; Accepted: 28 July 2020; Published: 30 July 2020

**Abstract:** Cotton root rot (CRR), caused by the fungus *Phymatotrichopsis omnivora*, is a destructive cotton disease that mainly affects the crop in Texas. Flutriafol fungicide applied at or soon after planting has been proven effective at protecting cotton plants from being infected by CRR. Previous research has indicated that CRR will reoccur in the same regions of a field as in past years. CRR-infected plants can be detected with aerial remote sensing (RS). As unmanned aerial vehicles (UAVs) have been introduced into agricultural RS, the spatial resolution of farm images has increased significantly, making plant-by-plant (PBP) CRR classification possible. An unsupervised classification algorithm, PBP, based on the Superpixel concept, was developed to delineate CRR-infested areas at roughly the single-plant level. Five-band multispectral data were collected with a UAV to test these methods. The results indicated that the single-plant level classification achieved overall accuracy as high as 95.94%. Compared to regional classifications, PBP classification performed better in overall accuracy, kappa coefficient, errors of commission, and errors of omission. The single-plant fungicide application was also effective in preventing CRR.

**Keywords:** precision agriculture; UAV; disease detection; cotton root rot; plant-level; single-plant; plant-by-plant; classification; image analysis; machine learning
