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

Abstract

Pulse crops, such as chickpea (Cicer arietinum L.), serve as excellent food sources that provide proteins and minerals to humans and livestock. Additionally, pulses are important sources of soil nitrogen in rotational cropping systems. However, pulse production is limited by several biotic and abiotic stress factors. One such disease is Ascochyta blight (Ascochyta rabiei) in chickpea. To minimize the impact of Ascochyta blight, timely information on disease outbreak and epidemics is essential for implementing disease control methods. Thus, in this study, the feasibility of monitoring Ascochyta blight disease severity in chickpea using remote sensing techniques was evaluated. Disease severity was monitored using an unmanned aircraft system integrated with different types of sensors (3-band multispectral, 5-band multispectral, and thermal cameras). Results indicated that different flight altitudes (60 m and 90 m above ground level) that lead to different image resolutions did not influence disease detection efficiency, especially with the 3-band camera. Selected image features, including canopy area, percentage of canopy area, and vegetation indices (e.g., green normalized difference vegetation index) from multispectral cameras, and mean canopy temperature from the thermal camera, were significantly correlated with yield and visual ratings of disease severity. Moreover, hyperspectral sensing was found to be useful in predicting disease severity. In summary, this study demonstrated that disease severity of Ascochyta blight in chickpea can be monitored using remote sensing methods under active field conditions. With timely and accurate disease severity information from high-throughput phenotyping technologies, the effects of Ascochyta blight on chickpea yield and quality can be minimized with timely application of proper management techniques.