High-throughput field phenotyping of Ascochyta blight disease severity in chickpea

Authors: ZHANG, CHONGYUAN - Washington State University; Chen, Weidong; SANKARAN, SINDHUJA - Washington State University

Submitted to: Crop Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/16/2019
Publication Date: 7/17/2019
Citation: Zhang, C., Chen, W., Sankaran, S. 2019. High-throughput field phenotyping of Ascochyta blight disease severity in chickpea. Crop Protection. 125. https://doi.org/10.1016/j.cropro.2019.104885.
DOI: https://doi.org/10.1016/j.cropro.2019.104885

Interpretive Summary: Ascochyta blight caused by the fungal pathogen Ascochyta rabiei is a devastating disease of chickpea worldwide. Management of the disease is through using resistant cultivars and timely application of fungicides. Although a variety of effective fungicides are available, timely application of the fungicides requires early and accurate detection of the disease in fields. Traditional detection methods requires regular field scouting which is expensive and require specific training on recognizing and identifying the disease. Using remote sensing with an unmanned aerial vehicle has not been explored in detecting Ascochyta blight. This research is the first time unmanned aerial vehicles have been used to detect Ascochyta blight of chickpea. It was found that flight altitude between 60 m and 90 m resulting in different image resolution did not influence disease detection efficiencies. In addition, selected image features such as canopy area from multispectral cameras, and mean canopy temperature from thermal cameras, were significantly correlated with visual rating of disease severity and yield. This study demonstrated that disease severity of Ascochyta blight of chickpea can be monitored using remote sensing in field conditions. With timely and accurate information of disease severity acquired from high-throughput phenotyping technologies, the adverse effect of Ascochyta blight on chickpea yield can be minimized by timely applying proper management techniques such as fungicide spraying.

Technical Abstract: Chickpea is an excellent food source of proteins and minerals for both human and livestock, and it provides important source of nitrogen in multiple cropping systems. However, chickpea production is limited by several biotic and abiotic factors such as Ascochyta blight (Ascochyta rabiei). To minimize the impact of Ascochyta blight on chickpea, timely information on outbreak and epidemics of Ascochyta blight is essential for implementation of disease control methods. Thus, in this study, the feasibility of monitoring of disease severity (Ascochyta blight) in chickpea using remote sensing techniques was evaluated. Disease severity was monitored using an unmanned aerial vehicle (UAV) integrated with different types of sensors (3-band multispectral, 5-band multispectral, and thermal cameras). The results indicated that flight altitude (60 m and 90 m above ground level) resulting in different image resolution did not influence disease detection efficiencies. In addition, selected image features such as canopy area, percentage of canopy area and vegetation indexes (e.g. green normalized difference vegetation index) from multispectral cameras, and mean canopy temperature from thermal camera, were significantly correlated with visual rating of disease severity and yield. In addition, hyperspectral sensing was also found to be useful in disease severity predictions. In summary, this study demonstrated that disease severity of Ascochyta blight of chickpea can be monitored using remote sensing in field conditions. With timely and accurate information of disease severity acquired from high-throughput phenotyping technologies, the effect of Ascochyta blight on chickpea yield and quality can be minimized by timely applying proper management techniques.