Precise maize detasseling base on oriented object detection for tassels

Abstract

The maize detasseling process was gradually automated with the continuous promotion of the maize detasseling machine. Nevertheless, some problems are gradually becoming more prominent for it. Most maize detasseling machine has a simple maize tassels detection system. That is, the height of the crop was identified, and not the specific information on the maize tassels, leading to low detasseling precision and a high leaf injury rate during maize detasseling machine working. Aiming at these issues, this paper proposed a novel method for the maize tassel pose estimation based on computer vision and oriented object detection. Specifically, a two-step framework for maize tassel pose estimation is developed. Firstly, the maize plant is captured from above, then the maize tassels and the second leaf’s vein are posed in the horizontal plane, and their poses are matched to the oriented bounding box. Second, an oriented bounding box is generated according to the Oriented R-CNN model detection. Since maize tassels in the field differ in size and the morphological color of different growth stages, estimating the maize tassel pose accurately by the angle of the oriented bounding box alone is unfeasible. Therefore, these pixels of the oriented bounding box were put into the Look Twice module to extract the critical information about maize tassels, which can accurately determine the final pose of the maize tassels. Finally, evaluation metrics on the test set indicate the proposed method performed with correct maize tassels pose estimation rate of 88.56% and 29.57 Giga Floating-point Operations (GFLOPs), which indicated that the feasibility of maize tassel poses estimation using the proposed method. This study provides the possibility and foundation for precise maize detasseling in maize detasseling machines.