A Lawn Weed Detection in Winter Season Based on Color Information

In this work, we propose a lawn weed detection method based on simple and fast color image processing for the case that color of weeds and lawns are clearly different, especially in winter. The proposed detection method is evaluated with two types of simulated automatic weeding systems, i.e., chemical and non-chemical (pulse high voltage discharge) based system. For chemical based, the detection method can destroy weeds of more than 91.48% with correct spray rate of 93.22% and herbicide reduction rate of 93.72%. For non-chemical based, 70.21% of weeds can be destroyed with 98.18% of sparking accuracy; only 3 times of false sparking. From the results, the performances of the proposed method are better than those of conventional gray-scale based detection methods when detects weeds in winter dataset. We also propose a method for deciding from an input image whether the color information based method should be employed. By testing with four image datasets taken from four different seasons, this method can completely discriminate winter dataset from the others. Consequently, a hybrid method, i.e., a combination between a gray-scale based detection method and the color based method, can be realized.