CropPainter: an effective and precise tool for trait-to-image crop visualization based on generative adversarial networks
Lingfeng Duan,
Zhihao Wang,
Hongfei Chen,
Jinyang Fu,
Hanzhi Wei,
Zedong Geng,
Wanneng Yang
Affiliations
Lingfeng Duan
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Zhihao Wang
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Hongfei Chen
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Jinyang Fu
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Hanzhi Wei
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Zedong Geng
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Wanneng Yang
National Key Laboratory of Crop Genetic Improvement, Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, and College of Engineering, Hubei Hongshan Laboratory, National Center of Plant Gene Research, Huazhong Agricultural University
Abstract Background Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. Results This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. Conclusion In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.
