REAL-TIME MACHINE VISION WEED-SENSING

Much work has been done to employ machine vision technology to sense weeds in crop fields. However, the use of machine vision weed-sensing with real-time objectives under variable outdoor lighting conditions is a relatively new area. This paper documents an effort to develop real-time weed sensing technologies using machine vision under variable lighting conditions. Images were acquired of weeds between rows of soybeans. Unsupervised learning by cluster analysis was used to classify pixels according to their color. This classified data was then used to train a Bayes classifier which was used to create a look-up table for real-time segmentation. Adaptive scanning with embedded segmentation was used to estimate weed population. These estimates were compared with manual weeds counts. The elapsed time to do this processing was measured to see if the real-time requirements were met.

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