A study on geospatial technology for detecting and mapping of Solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton crop

Abstract

Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researchers have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. The objective of the present study was to evaluate the relation between the mealybug severity and remote sensing indices and development of a model for mapping of mealybug damage using remote sensing indices. The mealybug-infested cotton crop had a significantly lower reflectance (33%) in the near infrared region and higher (14%) in the visible range of the spectrum when compared with the non-infested cotton crop having near infrared and visible region reflectance of 48 % and 9% respectively. Multiple Linear regression analysis showed that there were varying relationships between mealybug severity and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.63 to0.31. Model developed in this study for the mealybug damage assessment in cotton crop yielded significant relationship (r2=0.863) and was applied on satellite data of 21st September 2009 which revealed high severity of mealybug and it was low on 24th September 2010 which confirmed the significance of the model and can be used in the identification of mealybug infested cotton zones. These results indicate that remote sensing data have the potential to distinguish damage by mealybug and quantify its abundance in cotton.

Artificial-intelligence and sensing techniques for the management of insect pests and diseases in cotton: a systematic literature review

“…To evaluate the severity of mealybug, Singh et al . (2016) developed a model to map mealybug damage using remote-sensing indices. They used multiple linear regression for data analysis and evaluated the relationship between spectral vegetation indices (SVIs) and severity index.…”

“…Remote sensing has allowed us to obtain monitoring data, in real-time, of diseases and insect pests (Singh et al ., 2016). This allows us to provide an overview for large areas using, for example, satellites, airplanes and UAV platforms (Ranjitha et al ., 2014; Song et al ., 2017; Xavier et al ., 2019).…”

“…Satellite. To evaluate mealybug severity, Singh et al (2016) evaluated the relationship between mealybug severity and remotesensing indices. The authors used Landsat TM5 satellite images with spectral bands RGB, NIR, shortwave infrared and thermal.…”