An improved fast fuzzy c-means using crow search optimization

An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural

In this article is introduced an improved version of Fast Fuzzy C-Means (FFCM) by using the Crow Search optimization Algorithm (CSA) for the task of data clustering. In the proposed version of the FFCM the CSA is employed to find the centroids of the clusters that provide more accurate results in the clustering process. In this sense, the CSA avoids that the FFCM get stuck in local minima and increases the computational performance. The main feature of the CSA is its ability to find the global solution in complex optimization problems with the calibration of a reduced amount of parameter. This fact reduces the sensibility in the iterative process and permits the FFCM use the best centroids. To verify the efficiency of the proposed FFCM in a real problem, it is applied for the identification of maize plants in images from crop fields. The process starts using the adaptive colour histogram equalization to enhance the contrast and adjusts the intensities of crop images. The next task consists in to obtain the green index that is used by the CSA to compute the centroids for the FFCM algorithm. To evaluate the performance of the presented approach, it has been tested over a set of images from maize fields with different degrees of complexity, captured by different cameras and with a different perspective of the scene. Different metrics and a statistical analysis evidence that the proposed algorithm obtains better segmentation results in comparison with other methods. The experimental results proved that the proposed approach is capable of finding the optimal centroid values of the FFCM and avoids the local optima problem. Moreover, the FFCM based on CSA method can locate crop rows with the maximum accuracy as possible in digital images.

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