Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images

Abstract

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage.

Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

“…Multiple software tools are required to complete a series of operations. At present, the software involved in UAV RS image processing includes DJI Terra (v3.0.2) [128], ENVI (Exelis Visual Information Solutions, Boulder, CO, USA) [21], ArcGIS 9.1 (Esri, Redlands, CA, USA) [21,112], Agisoft PhotoScan Professional (Agisoft LLC, St. Petersburg, Russia) [112], QGIS (QGIS, Open Source Geospatial Foundation, Chicago, IL, USA, https://www.qgis.org/) [84], Pix4D mapper (Pix4D Inc., Lausanne, Switzerland, http://www.pix4d.com/) [21,84], etc. The main four software models are shown in Figure 8.…”

New Trends and Challenges in Precision and Digital Agriculture

“…VIs as parameters or measures are used to assess plant growth and development activity. Based on indicators such as NDVI and GNDVI, it is possible to identify diseases: for example, in cotton cultivation [68]. NDVI has been shown to be strongly correlated with potassium and phosphorus content in the soil [69].…”

Monitoring of Cotton Boll Opening Rate Based on UAV Multispectral Data

“…This includes applications like pest and disease mon itoring [23], estimation of crop growth dynamics and yield [24], and inversion of physio logical and biochemical traits such as leaf area index and chlorophyll content [25]. Wang et al achieved favorable results in identifying cotton wilt disease using multispectra UAVs [26], while Chen et al predicted cotton leaf stripping rates and generated prescrip tion maps based on remote sensing imagery [27].…”