Image Recognition Used In Crown Detection For Orchard Spraying System

The heavy reliance on chemicals raise many enviromental and economic concerns, the triditonal sprayer and pesiticide application method could not suit the mordern orchard spraying. In order to reduce chemical use and get farmer free from hard work in orchard spraying, a simulated real-time machine vision based robotic orchard spraying system was builded, that include three parts: (1) real-time tree image catching system; (2) tree image preprocessing and recognition system; (3) image expression and message communication system. It took 0.579s for the hole system to processing one image (768 × 576 pixels) , representing a 3m by 3m region of one tree.The emphasis of this dissertation was laid on the study of tree image recognition. Firstly, color was used to distinguish tree crow from the background. For sake of finding the proper feature in color model, ultimate measurement accuracy(UMA) was introduced to decide which feature was appropriate. According this evaluation method, ExG1 was selected as the color feature in tree image segmentation. Secondly, textural feature analysis was used to identify tree crown. The image (768×576 pixels) was divided into rectangles(16×16 pixels). Energy and entropy deriving from GLCM and coordinate(x,y) of each rectangle were selected as index of vector which represent every rectangles. The man-made image which include four different texture (soil, leafs of fruit tree, grassplot and sky) are tested, the result showed that each kind of texture rectangel was recognised with 100%. But if the vector of rectangel without coordinate, the accurateness of recognition was different (from 4% to 90%). So the coordinate is very important to textural analysis.