DEVELOPMENT OF NEURAL NETWORKS FOR WEED RECOGNITION IN CORN FIELDS

摘要

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