Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only at locations where CRR exists, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing from manned aircraft is an effective means of delineating CRR-infected field areas. In 2015, an unmanned aerial vehicle was used to collect high-resolution remote-sensing images in a field known to be infected with CRR. A method was developed to produce a prescription map (PM) from these data, and in 2017, fungicide was applied based on a PM derived from the 2015 image data. The results showed that the PM reduced the fungicide applied by 88.3%, with a reduction in CRR area of 90% compared to 2015. A simple economic model suggested that it is generally better to treat an entire CRR-infested field rather than leaving it untreated, and application based on a PM becomes preferable as the size of the farm and the yield increase while the CRR-infestation level and the number of fields on the farm decrease.
Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicle (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect information in a large field setup. An autonomous mobile GCP and a cooperation strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels for every two successive flight paths without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from -1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs.
The fungus Phymatotrichpsis omnivora, also called cotton root rot (CRR), is one of the most deadly cotton diseases in the Southwest U.S. Once the cotton is infected by CRR it is very unlikely for it to be cured. Previous research indicates that the CRR will reoccur at a similar area as previous years. A fungicide known as Topguard Terra was proven efficient in CRR prevention. Therefore, knowing the historical CRR-infested area is helpful to prevent CRR from appearing again in the future. The CRR-infested plants can be detected by using aerial remote sensing. When an unmanned aerial vehicle (UAV) was introduced to a remote sensing research field, the spatial and temporal resolution of imagery data increased significantly and higher precision CRR classification was made possible. A plant-by-plant (PBP) level classification based on the Superpixel concept was developed to identify CRR-infested and healthy cotton plants in the field at the single plant level. The PBP classification algorithm was improved to achieve fewer misclassifications.
In many fields in the southwestern U.S. and Mexico, a soil-borne fungus (Phymatotrichopsis omnivorum) causes a disease called cotton root rot (CRR) that can devastate a cotton crop by infecting the roots and destroying large numbers of cotton plants. In the last few years a fungicide treatment including the chemical, flutriafol, has proven effective at protecting cotton plants from CRR infection. However, the fungicide is expensive, and growers desire to minimize input costs and environmental risks, so it is desirable to treat only the portions of the field susceptible to CRR infection. Remote sensing with high-resolution satellites and manned aircraft has enabled delineation of the full extent of the disease late in the growing season. Recently, classified images have been used effectively to create prescription maps for variable-rate application of fungicide when planting a cotton crop in subsequent years. In 2015 a UAV was used to create a high-resolution image mosaic of a CRR-infected field at Thrall, Texas. The mosaic was classified into healthy and CRR-infected small zones, and a prescription map was created from the mosaic for variable-rate fungicide application during planting in 2017. The method proved as effective as uniform application across the field would have been. Furthermore, image-analysis techniques were developed that enable classification of image mosaics at approximately the single-plant level. Thus in the future it is conceivable that precision application of flutriafol during planting to prevent cotton root rot could be done at the level of a single seed.