[1] 梁伟军. 农业与相关产业融合发展研究. 华中农业大学博士学位论文, 湖北武汉, 2010.

Liang W J. Research on the Integration and Development of Agriculture and Related Industries. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 2010 (in Chinese with English abstract).

[2] 赵春江, 陆声链, 郭新宇, 肖伯祥, 温维亮. 数字植物及其技术体系探讨. 中国农业科学, 2010, 43: 2023–2030.

Zhao C J, Lu S L, Guo X Y, Xiao B X, Wen W L. Exploration of digital plants and their technical system. Sci Agric Sin, 2010, 43: 2023–2030 (in Chinese with English abstract).

[3] 张洪程, 王夫玉. 中国水稻群体研究进展. 中国水稻科学, 2001, 15: 51–56.

Zhang H C, Wang F Y. Research progress on rice population in China. Chin J Rice Sci, 2001, 15: 51–56 (in Chinese with English abstract).

[4] 程式华, 廖西元. 中国超级稻研究: 背景、目标和有关问题的思考.中国稻米, 1998, (1): 3–5.

Cheng S H, Liao X Y. Research on Chinese super rice: background, objectives, and reflections on related issues. Chin Rice, 1998, (1): 3–5 (in Chinese).

[5] 邹应斌, 周上游, 唐起源. 中国超级杂交水稻超高产栽培研究的现状与展望. 中国农业科技导报, 2003, 5(1): 31–35.

Zou Y B, Zhou S Y, Tang Q Y. The current status and prospects of research on super high yield cultivation of Chinese super hybrid rice. J Agric Sci Tech, 2003, 5(1): 31–35 (in Chinese with English abstract).

[6] Jung K H, An G, Ronald P C. Towards a better bowl of rice: assigning function to tens of thousands of rice genes. Nat Rev Genet, 2008, 9: 91–101.

[7] Alhnaity B, Kollias S, Leontidis G, Jiang S Y, Schamp B, Pearson S. An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth. Inform Sci, 2021, 560: 35–50.

[8] Yasrab R, Zhang J, Smyth P, Pound M P. Predicting plant growth from time-series data using deep learning. Remote Sens, 2021, 13: 331.

[9] 张慧春, 王国苏, 边黎明, 郑加强, 周宏平. 基于光学相机的植物表型测量系统与时序生长模型研究. 农业机械学报, 2019, 50: 197–207.

Zhang H C, Wang G S, Bian L M, Zheng J Q, Zhou H P. Research on plant phenotype measurement system and temporal growth model based on optical cameras. J Agric Mach, 2019, 50: 197–207 (in Chinese with English abstract).

[10] 朱新广, 常天根, 宋青峰, 常硕其, 王重荣, 张国庆, 郭亚, 周少川. 数字植物：科学内涵、瓶颈及发展策略. 合成生物学, 2020, 1: 285–297.

Zhu X G, Chang T G, Song Q F, Chang S Q, Wang C R, Zhang G Q, Guo Y, Zhou S C. Plants: scientific connotation, bottlenecks, and development strategies. Syn Biol J, 2020, 1: 285–297 (in Chinese with English abstract).

[11] Lindenmeyer A. Mathematical models for cellular interactions in development. J Theor Biol, 1968, 18: 300–315.

[12] Leitner D, Klepsch S, Knie A, Schnepf A. The algorithmic beauty of plant roots: an L-System model for dynamic root growth simulation. Math Comput Model Dyn Sys, 2010, 16: 575–587.

[13] Espana M, Baret F, Aries F, Chelle M, Andrieu B, Prevot L. Modeling maize canopy 3D architecture: application to reflectance simulation. Ecol Model, 1999, 122: 25–43.

[14] Jallas E, Sequeira R, Martin P, Turner S, Papajorgji P. Mechanistic cirtual modeling: coupling a plant simulation model with a three-dimensional plant architecture component. Environ Model Assess, 2009, 14: 29–45.

[15] Qian B, Huang W J, Xie D H, Ye H C, Guo A, Pan Y H, Jin Y, Xie Q Y, Jiao Q J, Zhang B Y, Ruan C, Xu T J, Zhang Y, Nie T G. Coupled maize model: a 4D maize growth model based on growing degree days. Comput Electron Agric, 2023, 212: 108124.

[16] Minorsky P V. Achieving the in silico plant. Systems biology and the future of plant biological research. Plant Physiol, 2003, 132: 404–409.

[17] Prusinkiewicz P. Modeling plant growth and development. Curr Opin Plant Biol, 2004, 7: 79–83.

[18] 肖亚, 李玉强. 农业人工智能综述. 数字技术与应用, 2020, 38(9): 204–205.

Xiao Y, Li Y Q. Overview of agricultural artificial intelligence. Digi Tech Appl, 2020, 38(9): 204–205. (in Chinese with English abstract).

[19] Fahlgren N, Gehan M A, Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol, 2015, 24: 93–99.

[20] Kim T H, Lee S H, Oh M M, Kim J O. Plant growth prediction based on hierarchical auto-encoder. Int Conf Electron Inform Comm IEEE, 2022, pp 1–3.

[21] Kim T H, Lee S H, Kim J O. A novel shape based plant growth prediction algorithm using deep learning and spatial transformation. IEEE Access, 2022, 10: 37731–37742.

[22] Sakurai S, Uchiyama H, Shimada A, Taniguchi R. Plant growth prediction using convolutional LSTM. VISIGRAPP, 2019, 14: 105–113.

[23] 王春颖, 泮玮婷, 李祥, 刘平. 基于STLSTM的植物生长发育预测模型. 农业机械学报, 2022, 53(6): 250–258.

Wang C Y, Pan W T, Li X, Liu P. A plant growth and development prediction model based on STLSTM. Trans CSAM, 2022, 53(6): 250–258 (in Chinese with English abstract).

[24] Wang C, Pan W, Song X, Yu H, Zhu J, Liu P, Li X. Predicting plant growth and development using time-series images. Agronomy, 2022, 12: 2213.

[25] Yasrab R, Zhang J, Smyth P, Pound M P. Predicting plant growth from time-series data using deep learning. Remote Sens, 2021, 13: 331.

[26] Drees L, Junker-Frohn L V, Kierdorf J, Roscher R. Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks. Comput Electron Agric, 2021, 190: 106415.

[27] Meng Y, Xu M, Yoon S, Jeong Y, Park D S. Flexible and high-quality plant growth prediction with limited data. Front Plant Sci, 2022, 13: 989304.

[28] Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun, 2014, 5: 5087.

[29] Wang T C, Liu M Y, Zhu J Y, Tao A, Kautz J, Catanzaro B. High-resolution image synthesis and semantic manipulation with conditional GANs. Comput Vis Pattern Recognit, 2017. pp 1–14.

[30] Sanghyun W, Jongchan P, Joon-Young L, In S K. CBAM: convolutional block attention module. Comput Vis Pattern Recognit, 2018. pp 3–19.

[31] Dowson D C, Landau B V. The fréchet distance between multivariate normal distributions. J Multivariate Anal, 1982, 12: 450–455.

[32] Korhonen J, You J. Peak signal-to-noise ratio revisited: is simple beautiful? Qlty Multimedia Exp IEEE, 2012. pp 37–38.

[33] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process, 2004, 13: 1–14.

[34] Wang X, Xie L, Dong C, Shan Y. Real-esrgan: training real-world blind super-resolution with pure synthetic data. IEEE Int Conf Comput Vis, 2021. pp 1905–1914.