Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing

Tianyi Wang,Robert L Nichols,J Alex Thomasson,Chenghai Yang,Thomas Isakeit

doi:10.3390/rs12081310

Abstract

Cotton root rot (CRR) is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Research has shown that CRR can be prevented or at least mitigated by applying a fungicide at planting, but the fungicide should be applied precisely to minimize the quantity of product used and the treatment cost. The CRR-infested areas within a field are consistent from year to year, so it is possible to apply the fungicide only at locations where CRR is manifest, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing (RS) from manned aircraft is an effective means of delineating CRR-infested field areas. Applying various classification methods to moderate-resolution (1.0 m/pixel) RS images has recently become the conventional way to delineate CRR-infested areas. In this research, an unmanned aerial vehicle (UAV) was used to collect high-resolution remote sensing (RS) images in three Texas fields known to be infested with CRR. Supervised, unsupervised, and combined unsupervised classification methods were evaluated for differentiating CRR from healthy zones of cotton plants. Two new automated classification methods that take advantage of the high resolution inherent in UAV RS images were also evaluated. The results indicated that the new automated methods were up to 8.89% better than conventional classification methods in overall accuracy. One of these new methods, an automated method combining k-means segmentation and morphological opening and closing, provided the best results, with overall accuracy of 88.5% and the lowest errors of omission (11.44%) and commission (16.13%) of all methods considered.

Highlights

Cotton root rot (CRR), caused by the fungus Phymatotrichopsis omnivora, is a major disease problem for cotton production in Texas and the southwestern U.S It was first observed in the 19th century, and it kills cotton and other dicots by preventing water and nutrients from being transported from roots to the rest of the plant [1]
684,758 pixels (24.09%) of healthy plants were overclassified into the CRR-infested class
The k-means andcSenVtMroidpsr.ocesses (KMSVM) classification results are at about the same accuracy level as the supervised classifications (Table 3)

Summary

Introduction

Cotton root rot (CRR), caused by the fungus Phymatotrichopsis omnivora, is a major disease problem for cotton production in Texas and the southwestern U.S It was first observed in the 19th century, and it kills cotton and other dicots by preventing water and nutrients from being transported from roots to the rest of the plant [1]. The fungus tends to occur in specific portions of fields and thrives in warm, moist, and alkaline (7.2–8.5) soil environments. If the disease occurs in the early stage of growth, the plant will die before bearing any fruit. CRR-infested areas in a field can expand to more than 50% of an entire field area during the season [3]. To apply the fungicide most efficiently, the CRR-infested areas must be identified. Because the CRR fungus is long-lived and colonizes specific areas of a field, the disease typically occurs at the same locations over many years, so future infested locations can be assumed to be consistent with historical position data. Three-band multispectral is widely available and a good candidate for practical application [9]

Methods

Results

Conclusion