The main objective of this project was to develop a weed recognition system based on artificial neural networks
to assist in the precision application of herbicides in corn fields. Digital images were collected in May 1998 using a
commercially available digital camera. The intensities of the three primary colors (red, green, and blue) were compared for
each pixel of the images. The three intensities of a pixel remained unchanged when, in the pixel, the green intensity was greater
than each of the other two; otherwise, the three intensities of the pixel were set to zero. Background objects, except plants,
were thus removed from the images. The resulting pixel intensities of the modified images were used as the inputs for Learning
Vector Quantization (LVQ) artificial neural networks (ANNs). ANNs were trained to distinguish corn from weeds, as well as
to differentiate between weed species. The success rate for a single ANN in distinguishing a given weed species from corn
was as high as 90%, and as high as 80% in distinguishing any of four weed species from corn. Better success rates might be
obtainable with more elaborate schemes for data input and/or structural improvements such as cascading. The
image–processing time for the ANNs was as short as 0.48 s per image, thus making it useful for real–time data processing
and application of herbicides. The development of such ANNs for weed recognition could be useful in precision farming to
guide site–specific herbicide application and ultimately reduce the total amount of herbicide applied as well as lowering the
risk of pollution.