基于改进Pix2Pix-HD网络的多品种水稻生长可视化预测方法

doi:10.3724/SP.J.1006.2024.42016

Abstract

Abstract:

With the advancement of information technology and agricultural digitization, the visualized prediction of digital plant growth holds significant development potential. As an important food crop, achieving visualized growth prediction for rice is of great significance for analyzing its growth and development. However, due to the large size and complex morphology of rice, attaining high-precision and high-resolution visualized growth prediction has been challenging. This study proposes a growth prediction method for rice based on an improved Pix2Pix-HD network, achieving high-precision visualized growth prediction from the heading stage to the filling stage of rice. Comparative experiments were designed to verify the effectiveness of the model improvement scheme. The results showed that the FID, PSNR, and SSIM values between the predicted and real filling stage rice images in the test set were 24.75, 13.58, and 0.78, respectively. The average correlation coefficient between the predicted and actual phenotypes was 0.762, demonstrating good accuracy at different scales. The proposed data-driven rice growth prediction method can achieve high-resolution and high visual realism in rice growth visualization predictions, providing new solutions for rice growth forecasting.

Key words: rice, growth visualized prediction, generative adversarial network, Pix2Pix-HD, digital plants

DUAN Ling-Feng, WANG Xin-Yi, WANG Zhi-Hao, GENG Ze-Dong, LU Yun-Rui, YANG Wan-Neng. Growth visualized prediction method of multi-variety rice based on improved Pix2Pix-HD network[J].Acta Agronomica Sinica, 2024, 50(12): 3083-3095.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Table 2

Table 3

Fig. 5

Table 4

References 34

[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, 闵绍楷. 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, Min S K. Research on Chinese super rice: background, objectives, and reflections on related issues. China 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 Technol, 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]	Alhnaity B, Pearson S, Leontidis G, Kollias S. Using deep learning to predict plant growth and yield in greenhouse environments. Int Soc Hortic Sci, 2019, 1296: 425-432.
[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(10): 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. Trans CSAE, 2019, 50(10): 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. doi: 10.12211/2096-8280.2020-018
	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]	Lindenmayer A. Mathematical models for cellular interactions in development. J Theor Biol, 1968, 18: 300-315. pmid: 5659072
[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. pmid: 12822566
[17]	Prusinkiewicz P. Modeling plant growth and development. Curr Opin Plant Biol, 2004, 7: 79-83. pmid: 14732445
[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. Digit Technol 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. doi: 10.1016/j.pbi.2015.02.006 pmid: 25733069
[20]	Kim T H, Lee S H, Oh M M, Kim J O. Plant growth prediction based on hierarchical auto-encoder. In: International Conference on Electronics, Information, and Communication. Jeju: 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. doi: 10.1038/ncomms6087 pmid: 25295980
[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]	Woo S, Park J, Lee J Y, Kweon I S. CBAM:convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, eds. Proceedings of the European Conference on Computer Vision. Munich: Springer Cham, 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?. In: Fourth International Workshop on Quality of Multimedia Experience. Melbourne: 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 Proc, 2004, 13: 600-612.
[34]	Wang X, Xie L, Dong C, Shan Y. Real-esrgan:training real-world blind super-resolution with pure synthetic data. In:Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021. pp 1905-1914.

Related Articles 15

[1]	Hu Zhao, Qian Run, Xie Feng-Pu, Ying Su-Ping. Genome-wide identification and expression analysis of the SPX gene family in rice under phosphorus treatment [J]. Acta Agronomica Sinica, 2026, 52(6): 1902-1912.
[2]	Zou Yi-Mei, Xu Min, Wang Hai-Yang, Yao Hui, Wang Jia-Feng, Liu Hao, Ren Dai-Sheng. Analysis of transcription factor regulatory networks in two-line male sterile rice seedling roots in response to salt stress [J]. Acta Agronomica Sinica, 2026, 52(6): 1728-1742.
[3]	Yan An, Jiang Kun-Wei, Wang Rong-Yuan, Tian Lin, Zhang Lu, Wang Yun, Xu Jian-Long. Identification and cloning of SVN7 controlling small vascular bundle number in the rice flag leaf [J]. Acta Agronomica Sinica, 2026, 52(5): 1364-1372.
[4]	Chen Wei, Wei Wan-Juan, Zhao Qi-Bing, Chang Dong-Wei, Yu Ling-Bo, Zhai Peng-Fei, Feng Zhi-Ming, Chen Zong-Xiang, Ren Yang-Tao, Yang Peng, Liu Hai-Lang, Li Zhen-Fu, Yang Yong-Le, Jin Yan-Gang, Zuo Shi-Min. Developing new germplasm of high-quality and early-maturing rice by editing Hd6 via CRISPR/Cas9 [J]. Acta Agronomica Sinica, 2026, 52(4): 1046-1056.
[5]	Shi Shao-Jie, Liu Kai, Chen Zi-Yi, Wang Hui-Ying, Li San-He, Zhou Lei, You Ai-Qing. Cloning and functional analysis of the dwarf and multi-tiller gene DMT1 in rice [J]. Acta Agronomica Sinica, 2026, 52(4): 1022-1034.
[6]	Liu Chang-You, Wang Shen, Shi Hui-Ying, Shen Ying-Chao, Sun Lei, Wang Yan, Zhang Zhi-Xiao, Su Qiu-Zhu, Tian Jing, Fan Bao-Jie. QTL mapping for bruchid resistance in an adzuki bean distant hybridization population using rice bean genetic resources [J]. Acta Agronomica Sinica, 2026, 52(3): 936-944.
[7]	Ye Fan, Li Shuai, Li Si-Yu, Chen Yun, Dou Chao-Yin, Liu Li-Jun. Effects of water-saving irrigation on rice yield and population quality in Northeast China [J]. Acta Agronomica Sinica, 2026, 52(3): 895-907.
[8]	Liu Ning, Fan Ping, Wang Cheng, Chen Qi-Qi, Cheng Qing-Yue, Tie Xia-Na, Tang Jing-Sha, Liu Bin-Bin, Xie Hong-Kun, Wang Jia-Yue, Shi Yuan-Qing, Ma Jun. Effects of reduced nitrogen application combined with organic fertilizer on yield formation and nitrogen utilization in mechanically transplanted rice [J]. Acta Agronomica Sinica, 2026, 52(3): 866-880.
[9]	Qin Yi-Yan, Fu Yao, Su Chang, Li Na, Xu Jing-Ru, Cheng Xiao-Ran, Zhang Qi, Zhao Ming-Hui. Functional analysis of OsST41 regulating salt tolerance in rice seedlings [J]. Acta Agronomica Sinica, 2026, 52(3): 802-812.
[10]	Zhu Jin-Juan, Wang Hui-Ping, Yang Guo-Dong, Wang Yu-Cheng, Yang Chen, Wang Bin, Agustiani Nurwulan, Tu Jun-Ming, Bi Jun-Guo, Cui Ke-Hui, Huang Jian-Liang, Peng Shao-Bing, Yuan Shen. Effects of water management and variety type on grain yield and quality in ratoon rice [J]. Acta Agronomica Sinica, 2026, 52(1): 295-315.
[11]	WANG Chan, WU Ying-Ying, LI Wen-Qi, LI Xia, WANG Fang-Quan, ZHOU Tong, YANG Jie. Development of functional markers of rice stripe disease resistance gene STV11 based on HRM technique [J]. Acta Agronomica Sinica, 2025, 51(9): 2547-2556.
[12]	GUO Bao-Wei, WANG Wang, WANG Kai, WANG Yan, ZENG Xin, JING Xiu, WANG Jing, NI Xin-Hua, XU Ke, ZHANG Hong-Cheng. Population dynamic characteristics and formation mechanisms of super high-yielding of two types of glutinous rice in the middle and lower reaches of the Yangtze Rive [J]. Acta Agronomica Sinica, 2025, 51(9): 2433-2453.
[13]	CHEN Hui-Ying, HE Jia-Xin, ZHU Bin, HUANG Shi-Xuan, ZHOU Xing-You, WU Jun-Quan, YANG Mei-Yan. Whole genome analysis and biological characterization of phage vB_XaS_ HDB2 infected with Xanthomonas oryzae pv. oryzae [J]. Acta Agronomica Sinica, 2025, 51(8): 2087-2099.
[14]	YANG Hai-Yang, WU Lin-Xuan, LI Bo-Wen, SHI Han-Feng, YUAN Xi-Long, LIU Jin-Zhao, CAI Hai-Rong, CHEN Shi-Yi, GUO Tao, WANG Hui. OsWRI3, identified based on QTL mapping, regulates seed shattering in rice [J]. Acta Agronomica Sinica, 2025, 51(7): 1712-1724.
[15]	WANG Fen, WU Dong-Li, ZHANG Quan-Jun. Response of phenological phase stages of single-cropping rice to climate change in Hubei province, China [J]. Acta Agronomica Sinica, 2025, 51(7): 1934-1948.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

评价指标 Evaluation indicators	形态特征相关系数 Correlation coefficient of morphological traits						纹理特征相关系数 Correlation coefficient of texture traits											MCC_M	MCC_T	MCC_A
	TPA	GPA	YPA	FD	H	W	直方图特征 Histogram						灰度梯度共生矩阵特征 Gray-level co-occurrence matrix
	TPA	GPA	YPA	FD	H	W	M	SE	S	MU3	U	E	C	G_M	GD_M	G_MSE	GD_MSE
CC	0.77	0.69	0.82	0.78	0.58	0.53	0.84	0.86	0.85	0.86	0.77	0.79	0.79	0.62	0.80	0.80	0.81	0.70	0.80	0.76
CC_S	0.15	0.21	-0.05	0.17	0.01	0.27	0.06	0.03	0.06	-0.02	0.16	0.16	-0.08	-0.08	-0.05	-0.01	-0.02	0.13	0.02	0.06

分辨率 Resolution	视觉相似性Visualized similarity			表型准确性Phenotypic accuracy
分辨率 Resolution	Fréchet距离FID	峰值信噪比PSNR	结构相似度SSIM	MCC_M	MCC_T	MCC_A
1024×1024	24.75	13.58	0.78	0.70	0.80	0.76
512×512	21.51	14.47	0.73	0.69	0.78	0.75
256×256	40.69	15.77	0.68	0.67	0.70	0.69

模型 Model	模型改进设计方案 Model improvement					视觉相似性 Visualized similarity			表型准确性 Phenotypic accuracy
模型 Model	训练阶段随机失活 Training dropout	测试阶段随机失活 Testing dropout	假标签损失 Fake label loss	空间注意力机制 Spatial attention	通道注意力机制 Channel attention	Fréchet距离 FID	峰值信噪比 PSNR	结构相似度 SSIM	MCC_M	MCC_T	MCC_A
模型1 Model 1	√	√	×	×	×	30.44	13.27	0.77	0.64	0.72	0.69
模型2 Model 2	×	×	×	×	×	26.10	13.76	0.78	0.62	0.74	0.70
模型3 Model 3	√	×	×	×	×	37.60	13.46	0.78	0.66	0.78	0.74
模型4 Model 4	√	×	√	×	×	29.65	13.58	0.78	0.65	0.78	0.74
模型5 Model 5	√	×	√	√	×	24.75	13.58	0.78	0.70	0.80	0.76
模型6 Model 6	√	×	√	√	√	32.80	13.72	0.78	0.68	0.81	0.76

模型及处理 Model and process	分辨率 Resolution	视觉相似性Visualized similarity			表型准确性Phenotypic accuracy
模型及处理 Model and process	分辨率 Resolution	Fréchet距离 FID	峰值信噪比 PSNR	结构相似度 SSIM	MCC_M	MCC_T	MCC_A
Pix2Pix模型 Pix2Pix model	256×256	45.60	14.53	0.65	0.50	0.63	0.58
RGVP + 1/4下采样 RGVP + 1/4 Down-sampling	256×256	40.69	15.77	0.68	0.67	0.70	0.69
Pix2Pix + 4倍超分辨 Pix2Pix + 4× super-resolution	1024×1024	68.91	13.31	0.76	0.49	0.64	0.59
RGVP模型 RGVP model	1024×1024	24.75	13.58	0.78	0.70	0.80	0.76

Growth visualized prediction method of multi-variety rice based on improved Pix2Pix-HD network

RichHTML431

PDF