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.