Automatic detection method of corn tassel traits

Abstract

The invention discloses an automatic detection method of corn tassel traits. The method comprises the following steps: performing target detection on an acquired farmland corn bottom view image at first to generate a tassel candidate frame to acquire a tassel potential area; subsequently, performing feature description and target detection on the tassel by utilizing a multi-view image characteristic and a Fisher vector coding method so as to confirm an affiliated area of the tassel; further completing the segmentation on tassel fine forms by utilizing semantic segmentation based on a detection result; finally establishing a mapping relation of the image characteristic and seven biomasses, such as length trait, width trait, perimeter trait, diameter trait, tassel color trait, branch quantity trait and total tassel quantity trait which have the physical significance. According to the method, the growth state of the corn tassel can be continuously monitored in real time, so that the detection result is high in correction rate, and the method has the great significance in corn reproductive growth research, corn genomics and genetics research and yield estimation.

Description

technical field [0001] The invention belongs to the cross field of computer vision and agricultural meteorological observation, and more specifically relates to an automatic detection method for corn tassel traits, that is, the traits of corn tassels in the image are obtained by taking the field corn bottom-view image sequence as the object feature method. Background technique [0002] As one of the three major food crops in the world, corn is widely planted all over my country. Analysis and research on various traits of corn can help to establish the relationship between its traits and yield to obtain greater benefits, and can also provide a basis for the study of corn genetics and genetics. However, for a long time, the detection of various traits of maize has been mainly through manual detection, which is easily affected by subjective factors and inefficient; at the same time, the observation process will inevitably cause damage to the growth environment of maize; and be...

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Problems solved by technology

[0003] In 2011, Tang Wenbing and others published the paper "Recognition and Positioning of Maize Tassels Based on Binocular Stereo Vision" in the "Proceedings of the 2011 Academic Annual Conference of the Chinese Society of Agricultural Engineering" using image segmentation technology to locate and detect corn tassels Relevant research has been carried out, but the method in this paper only considers color features, which is suitable for small-scale detection under specific lighting and simple backgrounds. It is not robust to natural lighting and field environment detection, and this method does not obtain The refined traits of spikes; in the paper "An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency" published by Mengni Ye et al. on "Eighth International Symposium on Multispectral Image Processing and Pattern Recognition" in 2013 The target detection framework of HOG-SVM is adopted and the clues of time and space are added to detect corn tassels, and then the arrival time of corn earing stage is predicted according to the detection results. The shortcoming of the paper is that only the gradient feature information of the gray image is considered. , the adaptability to the complex background is not good, and the tassel is not segmented, and the tassel is obtained for a more refined description; 2014 Ferhat In the paper "Detecting corn tassels using computer vision and support vector machines" published on "Expert Systems with Applications", a more robust corn tassel detection method was introduced. Compared with the previous paper, it considered More robust shape and texture features, but essentially color-based image segmentation, not suitable for sequential images with large illumination changes, and does not map image detection results to biological traits with physical meaning; in summary As mentioned above, although there are currently many detection technologies related to corn tassels, due to the limitations of various methods or strategies, it is difficult to apply them in the actual field environment, and these methods have not established the relationship between image features and actual physical features. The meaning of the biomass mapping relationship

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