); [EMAIL_ADDRESS] (J.-P.Y. ); [EMAIL_ADDRESS] (Z.-Q.C. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (Z.-Y.W.) Xin-Yu Gao  
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; [EMAIL_ADDRESS] (S.-X.Y. ); [EMAIL_ADDRESS] (P.-F.Z. ); [EMAIL_ADDRESS] (X.-Y.G. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Q.-H.L. ); [EMAIL_ADDRESS] (J.-P.Y. ); [EMAIL_ADDRESS] (J.-P.Y. ); [EMAIL_ADDRESS] (Z.-Q.C.C. ); [EMAIL_ADDRESS] (Y.Y. ); [EMAIL_ADDRESS] (Z.-Y.W.) Xin-Yu Gao  
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; [EMAIL_ADDRESS] (S.-X.Y. ); [EMAIL_ADDRESS] (P.-F.Z. ); [EMAIL_ADDRESS] (X.-Y.G. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Q.-H.L. ); [EMAIL_ADDRESS] (J.-P.Y. ); [EMAIL_ADDRESS] (J.-P.Y. ); [EMAIL_ADDRESS] (Z.-Q.C. ); 520183081341@cau. The recognition of microscopic objects is a crucial step to realize automatic micromanipulation. Because of the complexity of microscopic environments, the recognition of a microscopic object is a complex task. [PERSON] et al. [2] proposed a model based on a Mask RCNN (Region Convolutional Neural Networks) to detect and segment the dense small objects in nuclei detection. [PERSON] and [PERSON] [3] used a Faster RCNN to realize object detection on malaria images. This kind of microscopic object recognition methods, which is based on deep learning, usually requires a large number of training samples, and it is difficult to achieve the best performance when the sample data set is small. [PERSON] [4] proposed a method based on the circular Hough transform and some morphological tools to detect cells in blood for the diagnosis of diseases. This method is mainly used for circular microscopic object detection, but it cannot recognize other microscopic objects with different shapes. [PERSON] et al. [5] trained a cascade of histograms of oriented gradients features to detect stomata in microscopic images with a precision of 91.68%. The detection rate of the cascade of distinct features can be further improved by multi-feature fusion. [PERSON] et al. [6; 7; 8; 9] successively proposed a method for needle tip automatic recognition and localization in cell micromanipulation using template matching, and they developed a detect-focus-track-servo (DFTS) algorithm for image-guided cell manipulation. [PERSON] and [PERSON] [10] combined visual feedback and force feedback to realize the automatic assembly of micro objects. [PERSON] and [PERSON] [11] used an optical flow method based on the sum-of-squared-differences to detect and track pipette, and they used the Hough transform method to detect the nuclei of embryos for achieving automatic cell injection. [PERSON] et al. [12] made use of the gradient-weighted Hough transform method to detect oocyte and its polar body to facilitate micromanipulation. However, in some micromanipulations cases, such as the ion flux measurement by moving an ion-selective microelectrode (ISME) [13; 14; 15], the above-mentioned methods are not applicable. During the in-situ ion flux measurement of a live tissue, the ISME must be kept 5-30 \(\upmu\)m away from the measured object to ensure accuracy. Hence, force feedback of automatic micromanipulation [16; 1; 17] is not appropriate for applications like this case. In addition, growth, e.g., roots with that of 1-5 \(\upmu\)m/min, may affect the distance between the ISME and the object to be measured when the living object is measured, with the ISME potentially being damaged and causing measurement failure. To address these problems, especially in measurement of ion flux by moving the ISME, as shown in Figures 1 and 2, we have proposed a microscopic object recognition method--when we precisely locate the ISME tip and detect the object to be measured, the relative distance between the ISME and the detected object in the microscopic image can be automatically and accurately calculated, even with complex noise interference between the ISME tip and the object to be measured. Figure 1: The principle of ion flux measurement.