Growth prediction of tomato seedlings based on causal LSTM and GAN
DOI:
https://doi.org/10.25165/ijabe.v18i3.8757Keywords:
growth prediction, tomato seedlings, LSTM, GAN, deep learningAbstract
The stable production of seedlings is very important for seedling growers. Predicting the growth of seedlings helps growers promptly adjust management strategies and production expectations. Traditional methods rely on historical growth data or assess current plant physiological parameters to estimate future growth. This study aims to predict future images directly from historical growth images of tomato seedlings. Specifically, a dataset of 10-d image sequences of tomato seedlings was collected. Then, an algorithm based on several neural networks was applied to predict the images of the next 5 d based on the images of the first 5 d. The algorithm was composed of a causal long short-term memory (LSTM) unit, a gradient highway unit (GHU), and a pix2pix unit. The experimental results showed that the introduction of a Generative Adversarial Network (GAN) further enhanced the clarity and realism of the predicted images, ensuring higher quality and more accurate visual results. From the perspective of image similarity, the average mean squared error (MSE) reached 394.97 and the average structural similarity (SSIM) reached 0.90 over 5 d. From the perspective of biological information, the average prediction errors of the plant area were 1.7, 1.4, 1.5, 0.9, and 3.2 cm2 over the 5 d, and the average prediction errors of plant height were 1.7, 1.9, 4.6, 6.9, and 4.5 mm, respectively. The extracted biological information such as plant area and height showed good following performance compared with the real growth information. The research results show that predicting future plant images from historical images has the potential to become a useful tool for nursery growers to adjust management strategies and production expectations. Keywords: growth prediction, tomato seedlings, LSTM, GAN, deep learning DOI: 10.25165/j.ijabe.20251803.8757 Citation: Zhang H D, Kaizu Y, Furuhashi K, Hu H M, Imou K. Growth prediction of tomato seedlings based on causal LSTM and GAN. Int J Agric & Biol Eng, 2025; 18(3): 51–57.References
Higashide T, Yasuba K I, Suzuki K, Nakano A, Ohmori H. Yield of Japanese tomato cultivars has been hampered by a breeding focus on flavor. HortScience Horts, 2012; 47: 1408-1411.
Luna-Maldonado A I, Vidales-Contreras J A, Rodríguez-Fuentes H. Editorial: Advances and trends in development of plant factories. Front. Plant Sci., 2016; 7: 1848.
Yu W Z, Zheng J F, Wang Y L, Ji F, Zhu B Y. Adjusting the nutrient solution formula based on growth stages to promote the yield and quality of strawberry in greenhouse. Int J Agric & Biol Eng, 2023; 16（2）: 57-64.
Yin J, Liu X Y, Miao Y L, Gao Y, Qiu R C, Zhang M, et al. Measurement and prediction of tomato canopy apparent photosynthetic rate. Int J Agric & Biol Eng, 2019; 12（5）: 156-161.
Moriyuki S, Fukuda H. High-throughput growth prediction for Lactuca sativa L. seedlings using chlorophyll fluorescence in a plant factory with artificial lighting . Frontiers in plant science, 2016; 7: 394.
Kocian A, Massa D, Cannazzaro S, Incrocci L, Di Lonardo S, Milazzo P, Chessa S. Dynamic Bayesian network for crop growth prediction in greenhouses. Comput. Electron. Agric., 2020; 169: 105167.
Wang X, Yang Y, Zhao X, Huang M, Zhu Q B. Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM. Int J Agric & Biol Eng, 2023; 16（2）: 199-206.
Yamamoto K, Guo W, Ninomiya S. Node detection and internode length estimation of tomato seedlings based on image analysis and machine learning. Sensors, 2016; 16: 1044.
Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection. Front. Plant Sci., 2016; 7: 1419.
Agarwal M, Gupta S K, Biswas K K. Development of efficient CNN model for tomato crop disease identification. Sustain. Comput. Inform. Syst., 2020; 28: 100407.
Yuan Y, Chen L, Ren Y C, Wang S M, Li Y. Impact of dataset on the study of crop disease image recognition. Int J Agric & Biol Eng, 2022; 15（5）: 181-186.
Baheti H, Thakare A, Bhople Y, Darekar S, Dodmani O. Machine learning algorithm for detection and classification of tomato plant leaf disease. In: 2022 IEEE 7th International Conference for Convergence in Technology （I2CT）. IEEE, Mumbai, India, 2022; pp.1-7.
Liu J, Wang X. Tomato diseases and pests detection based on improved yolo V3 convolutional neural network. Front. Plant Sci., 2020; 11: 898.
Rustia D J A, Chao J J, Chiu L Y, Wu Y F, Chung J Y, Hsu J C, et al. Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method. J. Appl. Entomol., 2021; 145: 206-222.
Jin X, Zhu X W, Ji J T, Li M Y, Xie X L, Zhao B. Online diagnosis platform for tomato seedling diseases in greenhouse production. Int J Agric & Biol Eng, 2024; 17（1）: 80-89.
Li P, Zhong N, Dong W, Zhang M, Yang D T. Identification of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse. Int J Agric & Biol Eng, 2023; 16（6）: 226-235.
Jeong Y, Kim S, Yi K. Surround vehicle motion prediction using LSTM-RNN for motion planning of autonomous vehicles at multi-lane turn intersections. IEEE Open J. Intell. Transp. Syst., 2020; 1: 2-14. DOI: 10.1109/OJITS. 2020.2965969.
Tang Y, Ma L, Liu W, Zheng W S. Long-term human motion prediction by modeling motion context and enhancing motion dynamic. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, Stockholm, Sweden, 2018; pp.935-941. DOI: 10.48550/arXiv.1805.02513.
Karevan, Z, Suykens J A K. Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks, 2020; 125: 1-9.
Uchiyama H, Sakurai S, Mishima M, Arita D, Okayasu T, Shimada A, Taniguchi R I. An easy-to-setup 3D phenotyping platform for Komatsuna dataset. In: Proceedings of the IEEE international conference on computer vision workshops, 2017; pp.2038-2045.
Scharr H, Pridmore T, Tsaftaris S A. Computer vision problems in plant phenotyping, CVPPP 2017: Introduction to the CVPPP 2017 workshop papers. In: 2017 IEEE International Conference on Computer Vision Workshops （ICCVW）. IEEE, Venice, Italy, 2017; pp.2020-2021. DOI: 10.1109/ICCVW.2017.236
Sakurai S, Uchiyama H, Shimada A, Taniguchi R I. Plant growth prediction using convolutional LSTM. In: Kerren, A., Hurter, C., Braz, J. （Eds.）, VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SciTePress, Portugal, 2019; pp.105-113. DOI: 10.5220/0007404901050113.
Yasrab R, Zhang J, Smyth P, Pound M P. Predicting plant growth from time-series data using deep learning. Remote Sensing, 2021; 13: 331.
Kim T, 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.
Ueda M, Asano M, Sawai Y, Yamamura S. Leaf-movement factors of nyctinastic plant, Phyllanthus urinaria L., the universal mechanism for the regulation of nyctinastic leaf-movement. Tetrahedron, 1999; 55: 5781-5792.
Li Y, Zhu Z, Kong D, Han H, Zhao Y. EA-LSTM: Evolutionary attention-based LSTM for time series prediction. Knowl-Based Syst, 2019; 181: 104785.
Liu J, Xu L, Chen N. A spatiotemporal deep learning model ST-LSTM-SA for hourly rainfall forecasting using radar echo images. J. Hydrol., 2022; 609: 127748.
Fu R, Zhang Z, Li L. Using LSTM and GRU neural network methods for traffic flow prediction. In: 31st Youth Academic Annual Conference of Chinese Association of Automation （YAC）, IEEE, Wuhan, China, 2016; pp.324-328. DOI: 10.1109/YAC. 2016.7804912.
Oprea S, Martinez-Gonzalez P, Garcia-Garcia A, Castro-Vargas J A, Orts-Escolano S, Garcia-Rodriguez J, Argyros A. A review on deep learning techniques for video prediction. IEEE Trans. Pattern Anal. Mach. Intell., 2022; 44: 2806-2826.
Wang Y, Gao Z, Long M, Wang J, Yu P S. PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In: Dy J, Krause A. （Eds.） Proceedings of the 35th International Conference on Machine Learning. PMLR, Proceedings of Machine Learning Research, 2018; pp.5123-5132.
Srivastava R K, Greff K, Schmidhuber J. Training very deep networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. MIT Press, Montreal, Canada, 2015; pp.2377-2385. DOI: 10.48550/ arXiv.1507.06228.
Isola P, Zhu J Y, Zhou T, Efros A A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017; pp.1125-1134. DOI: 10.1109/CVPR.2017.632
Palubinskas G. Image similarity/distance measures: What is really behind MSE and SSIM? Int. J. Image Data Fusion, 2017; 8: 32-53.
Bakurov I, Buzzelli M, Schettini R, Castelli M, Vanneschi L. Structural similarity index （SSIM） revisited: A data-driven approach. Expert Syst. Appl., 2022; 189: 116087.
Downloads
Published
How to Cite
Issue
Section
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
