In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The evaluation results showed an accuracy of over 80% in identification of spikes. The spike detection step was further improved by removing noise using an area and height threshold. We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential.
The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield.