An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning
Lejun Yu,
Jiawei Shi,
Chenglong Huang,
Lingfeng Duan,
Di Wu,
Debao Fu,
Changyin Wu,
Lizhong Xiong,
Wanneng Yang,
Qian Liu
Affiliations
Lejun Yu
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Jiawei Shi
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Chenglong Huang
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Lingfeng Duan
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Di Wu
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Debao Fu
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Changyin Wu
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Lizhong Xiong
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Wanneng Yang
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China; Corresponding authors.
Qian Liu
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China; School of biomedical engineering, Hainan University, Haikou 570228, Hainan, China; Corresponding authors.
Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.
