Machine Vision System for Automatic Weeding Strategy using Image Processing Technique

The most widely used method for weed control is to use agricultural chemicals (herbicides products). This heavy reliance on chemicals raises many environmental and economic concerns, causing many plantation companies to seek alternatives for weed control in order to reduce chemical usage in their plantation. Since manual labor is costly and expensive, an automated weed control system may be economically feasible. A machine vision precision automated weed control system could also reduce the usage of chemicals. In this research, an intelligent real-time system for automatic weeding strategy in oil palm plantation using image processing has been developed to identify and discriminate the weed types namely as narrow and broad. In machine vision technology, the main component of the system is image processing to recognize type of weeds. Three techniques of image processing, involving statistical approach GLCM and structural approach FFT and SIFT, have been used and compared to find the best solution of weed recognition for classification. The developed machine vision system consists of a mechanical structure, which includes a sprayer, a Logitech web digital camera, 12v motor coupled with a pump system and a small size CPU as a processor. Offline images and recorded video has been tested to the system and classification result of weed shows the successful rate is above 80%.