Rice leaf detection with genetic programming

This paper describes an approach to the detection rice plants in images of rice fields by using genetic programming. The method involves the evolution of a genetic programming classifier of 20 × 20 pixel windows to distinguish rice and nonrice windows, applies the evolved classifier to each pixel position in a test image in a scanning window fashion and determines the class of a pixel by majority voting. The individual pixel values in the window comprise the terminal set. The four arithmetic operators, augmented by square root, comprise the function set. Fitness is a weighted sum of true positive and true negative rates. The classifier achieves an accuracy of 90% on positive and negative windows and is highly accurate in localizing rice leaves in test images for micro-spraying of nutritional supplements. The evolutionary approach clearly outperforms a thresholding approach based on colour which is unable to distinguish between rice an leaves.