Among different toxicants, pesticide is a menace to grapes. For the identification of pesticide in grapes,
conventional chemical methods are time consuming, expensive and may need specialized manpower.
This paper proposes an efficient image processing based non-destructive method for classification of
pesticide treated and untreated (fresh) grapes. Before analysing the grape quality by imaging based
technique, the pesticide content of untreated and treated grapes were analysed through LC-MS/MS. A
region of interest from the image is segmented from the bunch of grapes and some discriminatory
features are extracted in frequency domain using Haar filter. Features are selected up to the third level of
decomposition in wavelet domain and analyzed for discriminatory behaviour. The variation in the features
of the images is related to the difference between pesticide treated and untreated grapes. These
statistical features are then analyzed and used for identification of pesticide content in these samples
using a support vector machine (SVM) classifier. The experimental results indicate that the proposed
method is efficient for identification of untreated grapes and pesticide treated grapes from the features of
the images. The accuracy of identification of pesticide treated grapes is high and the computation time is
fast making this method suitable as a real time application for quality control in grapes