Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features

The variability of illumination and weather conditions lead to a big challenge for infield image segmentation. Therefore, robust, fast, and automated algorithms are highly required to obtain reliable image segmentation results. This research was aimed to develop efficient unsupervised clustering algorithms for oilseed rape image segmentation in the field. The Naive Bayes rule was first employed to select a supreme color feature from ten color models. An initialization approach based on the genetic algorithm (GA) was then used to define the initial cluster centroids for subsequent Gaussian mixture model (GMM), self-organizing map (SOM), fuzzy c-mean (FCM), and k-means algorithms. The length of the chromosome was determined using cluster validity indices. Finally, the performances of these algorithms were evaluated based on the image segmentation quality and computation time. After testing the proposed method on the image datasets from two fields, the results revealed that the highest segmentation accuracy of 96% was obtained using the optimized SOM and the lowest computation time was obtained using the k-means. The GA-based initialization speeded up the convergence process and ensured consistent labeling between runs. All clustering algorithms were proved to be robust to varying illumination conditions and can process images with a very complex background in an automated fashion.