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Plant stomata phenotypic traits can provide a basis for enhancing crop tolerance in adversity. Manually counting the number of stomata and measuring the height and width of stomata obviously cannot satisfy the high-throughput data. How to detect and recognize plant stomata quickly and accurately is the prerequisite and key for studying the physiological characteristics of stomata. In this research, we consider stomata recognition as a multi-object detection problem, and propose an end-to-end framework for intelligent detection and recognition of plant stomata based on feature weights transfer learning and YOLOv4 network. It is easy to operate and greatly facilitates the analysis of stomata phenotypic traits in high-throughput plant epidermal cell images. For different cultivars, multi-scales, rich background features, high density, and small stomata object images, the proposed method can precisely locate multiple stomata in microscope images and automatically give phenotypic traits of stomata. Users can also adjust the corresponding parameters to maximize the accuracy and scalability of automatic stomata detection and recognition. Experimental results on actual data provided by the National Maize Improvement Center show that the proposed method is superior to the existing methods in high stomata automatic detection and recognition accuracy, low training cost, strong generalization ability.


Xiao-Hui Yang, Zi-Jun Xi, Jie-Ping Li, Xin-Lei Feng, Xiao-Hong Zhu, Si-Yi Guo, Chun-Peng Song. Deep Transfer Learning-Based Multi-Object Detection for Plant Stomata Phenotypic Traits Intelligent Recognition. IEEE/ACM transactions on computational biology and bioinformatics. 2023 Jan-Feb;20(1):321-329

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PMID: 34941519

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