Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #334753

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Detection of Colorado potato beetle damage using remote sensing from small unmanned aircraft systems

item Hunt, Earle - Ray
item Rondon, S.i. - Oregon State University

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2017
Publication Date: 5/2/2017
Citation: Hunt Jr, E.R., Rondon, S. 2017. Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. Journal of Applied Remote Sensing. 11:16729.

Interpretive Summary: Colorado potato beetles (CPB) are one of the most destructive pests for potato crops, and these beetles quickly develop resistance to pesticides which increase the likelihood of potato crop failure. Integrated Pest Management seeks to reduce pesticide use by considering the whole agro-ecosystem and using other control methods to maintain low numbers of CPB. More control options are available when CPB infestations are detected early, which requires remote sensing data with very small pixel sizes. A multi-rotor drone with a small multispectral sensor collected images over experimental plots at Oregon State University’s Hermiston Agricultural Research and Extension Center. The drone’s flight altitude was 30 m and 60 m above ground level, so pixel sizes were 15 and 30 mm. On 23 June 2014, no CPB damage was apparent in the plant canopy, and on the next day, CPB damage was clearly visible. Plots were ranked visually from the least to the most damaged. We compared different methods for estimating area damaged with the numbers of CPB adults and larvae. The normalized difference vegetation index (NDVI) averaged over each plot was not related to the CPB damage ranking. Furthermore, the number of pixels in each plot with NDVI below a threshold of 0.8 was not related to the visual ranking of damage. Two new methods, object-based image analysis and estimation plant height, were tested and both successfully detected the amount of damage. However, more work on these methods is required in order to automate detection, so that large areas of drone imagery may be rapidly processed for integrated pest management.

Technical Abstract: Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other vegetables, and have developed resistance to most pesticides. Integrated pest management is a collection of control methods, including pesticides, with the aim of limiting insect damage to an acceptable level. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. An experiment was conducted at the Hermiston Agricultural Research and Extension Center in potato with 4 levels of CPB in a randomized block design. A six-rotor UAS was flown at altitudes of 60 m and 30 m a multispectral sensor and up-looking incident light sensor. After just one day, plant damage was visible, but not correlated with the total number of beetles per plot. Plot-mean NDVI and plot area classified with a threshold NDVI = 0.8 were not correlated with visible CPB damage. Therefore, traditional methods for using satellite data would not detect CPB damage until later in the growing season. However, plot area of CPB damage from object-based image analysis was highly correlated. Furthermore, plant height calculated using structure-from-motion point clouds was also correlated with CPB damage, but this method required extensive operator intervention for success. Object-based image analysis has potential for early detection based on high resolution sUAS remote sensing.