Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 163-173.doi: 10.12133/j.smartag.SA202201012

• Information Perception and Acquisition • Previous Articles    

Identification and Counting of Silkworms in Factory Farm Using Improved Mask R-CNN Model

HE Ruimin1(), ZHENG Kefeng2, WEI Qinyang1, ZHANG Xiaobin2, ZHANG Jun1, ZHU Yihang2, ZHAO Yiying2, GU Qing2()   

  1. 1.Shengzhou Mosang High-tech Co. , Ltd. , Shaoxing 312400, China
    2.Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
  • Received:2021-11-02 Online:2022-06-30 Published:2022-08-05
  • corresponding author: GU Qing E-mail:agri_intel@163.com;guq@zaas.ac.cn


Factory-like rearing of silkworm (Bombyx mori) using artificial diet for all instars is a brand-new rearing mode of silkworm. Accurate feeding is one of the core technologies to save cost and increase efficiency in factory silkworm rearing. Automatic identification and counting of silkworm play a key role to realize accurate feeding. In this study, a machine vision system was used to obtain digital images of silkworms during main instars, and an improved Mask R-CNN model was proposed to detect the silkworms and residual artificial diet. The original Mask R-CNN was improved using the noise data of annotations by adding a pixel reweighting strategy and a bounding box fine-tuning strategy to the model frame. A more robust model was trained to improve the detection and segmentation abilities of silkworm and residual feed. Three different data augmentation methods were used to expand the training dataset. The influences of silkworm instars, data augmentation, and the overlap between silkworms on the model performance were evaluated. Then the improved Mask R-CNN was used to detect silkworms and residual feed. The AP50 (Average Precision at IoU=0.5) of the model for silkworm detection and segmentation were 0.790 and 0.795, respectively, and the detection accuracy was 96.83%. The detection and segmentation AP50 of residual feed were 0.641 and 0.653, respectively, and the detection accuracy was 87.71%. The model was deployed on the NVIDIA Jetson AGX Xavier development board with an average detection time of 1.32 s and a maximum detection time of 2.05 s for a image. The computational speed of the improved Mask R-CNN can meet the requirement of real-time detection of the moving unit of the silkworm box on the production line. The model trained by the fifth instar data showed a better performance on test data than the fourth instar model. The brightness enhancement method had the greatest contribution to the model performance as compared to the other data augmentation methods. The overlap between silkworms also negatively affected the performance of the model. This study can provide a core algorithm for the research and development of the accurate feeding information system and feeding device for factory silkworm rearing, which can improve the utilization rate of artificial diet and improve the production and management level of factory silkworm rearing.

Key words: silkworm, artificial diet, accurate feeding, machine vision, deep learning, mask R-CNN, noise data

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