Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

Abstract

Wheat ear counting is a prerequisite for the evaluation of wheat yield. A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation. The frequency domain decomposition of wheat ear image is completed by multiscale support value filter (MSVF) combined with improved sampled contourlet transform (ISCT). Support Vector Machine (SVM) is the classic classification and regression algorithm of machine learning. MSVF based on this has strong frequency domain filtering and generalization ability, which can effectively remove the complex background, while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears. In order to improve the level of wheat yield prediction, MSVF-ISCT method is used to decompose the ear image in multi-scale and multi direction in frequency domain, reduce the interference of irrelevant information, and generate the sub-band image with more abundant information components of ear feature information. Then, the ear feature is extracted by morphological operation and maximum entropy threshold segmentation, and the skeleton thinning and corner detection algorithms are used to count the results. The number of wheat ears in the image can be accurately counted. Experiments show that compared with the traditional algorithms based on spatial domain, this method significantly improves the accuracy of wheat ear counting, which can provide guidance and application for the field of agricultural precision yield estimation.