Development of a herbicide application map using artificial neural networks and fuzzy logic

The primary objective in this project was to develop a precision herbicide-spraying system in a corn field. Ultimately, such a system would involve real-time image collection and processing, weed identification, mapping of weed density, and sprayer control using a digital camera. A proposed image processing method involving artificial neural networks was evaluated for image recognition accuracy, computer time and memory requirements. The greenness method, based on a pixel-by-pixel comparison of red–green–blue intensity values, was successfully developed. The recognition of weeds in the field was then simplified by taking images between the corn rows. The images were processed by the greenness method to obtain percent greenness in each image. This information was used to create weed coverage and weed patchiness maps. Based on these maps, herbicide application rates were determined for each spot in the field. This was done by using the weed coverage and weed patchiness maps as inputs to a simulated fuzzy logic controller, and integrating the output of the controller over the field area corresponding to the input images. Simulations using different fuzzy rules and membership functions indicated that the precision spraying has potential for reducing water pollution from herbicides needed for weed control in a corn field.