Estimating the Severity of Sugarcane Aphids Infestation on Sorghum with Machine Vision

Sugarcane aphid (SCA), Melanaphis sacchari, is one of the most prominent insect pests of grain, forage and bio-energy sorghum in the southern US since 2013. Â The timing and dosage of a pesticide application for SCA depend on a close monitoring of its pressure or severity change in the field. Â To assist the field scouting, digital images were taken using a smart phone in proximity of infected leaves and corresponding image processing algorithms were developed later to estimate the infestation severity in this study. Â Image samples were grouped into four classes according to the infestation severity for aphid management considerations; no threat (0-10 SCA/leaf), insecticide use should be considered (11-125 SCA/leaf), insecticide should be used and yield loss likely (126-500 SCA/leaf), and plant death possible (more than 500 SCA/leaf). Â With 5-fold cross validation, results showed that the best average classification accuracy across the four SCA classes was 85.0% with the modified OVO-SVM algorithm. Â The SCA quantification accuracies achieved in this study using the SVM algorithm showed the promise of using machine learning algorithms in this case of aphid density estimation on sorghum leaves. Â The methodology developed in this study can be modified with more sophisticated machine learning algorithms and more data in the future to be incorporated into a handheld or a mobile remote sensing system to assist growers and researchers with automatically quantifying SCA in a fast and objective manner.

In International Journal of Precision Agricultural Aviation