RICE YIELD PREDICTION USING MACHINE LEARNING
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Published:
Aug 16, 2024
Keywords:
multi-layer perceptron (MLP), random forests (RF), rice yield prediction, support vector machines (SVM), Vision Transformer (ViT)
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Abstract
This paper proposes a novel approach for predicting rice yield using rice field images. This approach utilizes the ability of Vision Transformer (ViT) architecture to extract meaningful features from field images for rice yield prediction. The model was first trained with a classification task. The standard Vision Transformer model is modified by replacing the classification layer with a custom regression layer designed to predict rice yield. This modified Vision Transformer model is then trained on field images with corresponding yield data. Various regression models, such as random forests (RF), support vector regressors (SVR), and multi-layer perceptrons (MLP), were employed to find the best regression model for rice yield prediction.
Over 11,000 digital images were collected during the ripening stage of rice plants in An Giang Province and Tra Vinh Province (Vietnam), with the rough grain yield recorded after harvest in these areas ranging from 5 to 12 t ha-1. The experimental results indicate that Vision Transformer – Random forests model achieved the lowest mean absolute error is 75.96.
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1.
Ha T-V, Do T-N, Nguyen B-A. RICE YIELD PREDICTION USING MACHINE LEARNING. journal [Internet]. 16Aug.2024 [cited 18Oct.2024];14(8). Available from: https://journal.tvu.edu.vn/index.php/journal/article/view/4271
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References
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trends in rice remote sensing over the past 42 years
(1980–2021). Remote Sensing. 2022;14(15): 3607.
https://doi.org/10.3390/rs14153607.
[2] Matsumura K, Hijmans RJ, Chemin Y, Elvidge CD,
Sugimoto K, Wu W, et al. Mapping the global supply
and demand structure of rice. Sustainability Science.
2009;4(2): 301–313. https://doi.org/10.1007/s11625-
009-0077-1.
[3] Wan L, Cen H, Zhu J, Zhang J, Zhu Y, Sun
D, et al. Grain yield prediction of rice using
multi-temporal UAV-based RGB and multispectral
images and model transfer – a case study of
small farmlands in the South of China. Agricultural and Forest Meteorology. 2020;291: 108096.
https://doi.org/10.1016/j.agrformet.2020.108096.
[4] Zhou X, Zheng HB, Xu XQ, He JY, Ge XK, Yao
X, et al. Predicting grain yield in rice using multitemporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2017;130: 246–
255. https://doi.org/10.1016/j.isprsjprs.2017.05.003.
[5] Son NT, Chen CF, Chen CR, Guo HY, Cheng
YS, Chen SL, et al. Machine learning approaches for rice crop yield predictions using timeseries satellite data in Taiwan. International Journal of Remote Sensing. 2020;41(20): 7868–7888.
https://doi.org/10.1080/01431161.2020.1766148.
[6] Sarkar TK, Roy DK, Kang YS, Jun SR, Park JW, Ryu
CS. Ensemble of machine learning algorithms for rice
grain yield prediction using UAV-based remote sensing. Journal of Biosystems Engineering. 2024;49(1):
1–19. https://doi.org/10.1007/s42853-023-00209-6.
[7] Pankaj, Kumar B, Bharti PK, Vishnoi VK, Kumar
K, Mohan S, et al. Paddy yield prediction based
on 2D images of rice panicles using regression
techniques. The Visual Computer. 2024;40(6): 4457–
4471. https://doi.org/10.1007/s00371-023-03092-6.
[8] Slafer GA, Savin R, Sadras VO. Coarse and
fine regulation of wheat yield components
in response to genotype and environment.
Field Crops Research. 2014;157: 71–83.
https://doi.org/10.1016/j.fcr.2013.12.004.
[9] Lu H, Cao Z, Xiao Y, Fang Z, Zhu Y, Xian
K. Fine-grained maize tassel trait characterization with multi-view representations. Computers
and Electronics in Agriculture. 2015;118: 143–158.
https://doi.org/10.1016/j.compag.2015.08.027.
[10] Ferrante A, Cartelle J, Savin R, Slafer GA. Yield
determination, interplay between major components
and yield stability in a traditional and a contemporary wheat across a wide range of environments. Field Crops Research. 2017;203: 114–127.
https://doi.org/10.1016/j.fcr.2016.12.028.
[11] Jin X, Liu S, Baret F, Hemerlé M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment. 2017;198: 105–114.
https://doi.org/10.1016/j.rse.2017.06.007.
[12] Wang X, Yang W, Xiong L, Huang C,
Liang X, Chen G, et al. Field rice panicle
detection and counting based on deep learning.
Frontiers in Plant Science. 2022;13: 966495.
https://doi.org/10.3389/fpls.2022.966495.
[13] Yang Q, Shi L, Han J, Zha Y, Zhu P. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely
sensed images. Field Crops Research. 2019;235: 142–
153. https://doi.org/10.1016/j.fcr.2019.02.022.
[14] Tanaka Y, Watanabe T, Katsura K, Tsujimoto Y, Takai
T, Tanaka TST, et al. Deep learning enables instant
and versatile estimation of rice yield using groundbased RGB images. Plant Phenomics. 2023;5: 0073.
https://doi.org/10.34133/plantphenomics.0073.
[15] Bellis ES, Hashem AA, Causey JL, Runkle BRK,
Moreno-García B, Burns BW, et al. Detecting intra-field variation in rice yield with unmanned aerial vehicle imagery and deep learning. Frontiers in Plant Science. 2022;13: 716506.
https://doi.org/10.3389/fpls.2022.716506.
[16] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn
D, Zhai X, Unterthiner T, et al. An image is worth
16x16 words: Transformers for image recognition at
scale. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria. 3–7 May 2021; Vienna, Austria. ICLR; 2021.
https://doi.org/10.48550/arXiv.2010.11929.
[17] Breiman L. Random forests. Machine Learning. 2001;45: 5–32.
https://doi.org/10.1023/A:1010933404324.
[18] Vapnik VN. The nature of statistical learning theory.
New York: Springer; 1995.
[19] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradientbased learning applied to document recognition.
Proceedings of the IEEE. 1998;86(11): 2278–2324.
https://doi.org/10.1109/5.726791.
[20] Deng J, Berg A C, Li K, Fei-Fei L. What does
classifying more than 10,000 image categories tell
us? In: Daniilidis K, Maragos P, Paragios N (eds.).
Computer Vision – ECCV 2010 – 11th European Conference on Computer Vision. 5–11 September 2010;
Heraklion, Crete, Greece. Berlin: Springer Berlin Heidelberg; 2010. p.71–84. https://doi.org/10.1007/978-
3-642-15555-0_6.
[21] Pedregosa F, Varoquaux G, Gramfort A, Michel
V, Thirion B, Grisel O, et al. Scikit-learn:
Machine learning in Python. Journal of Machine Learning Research. 2011;12: 2825–2830.
https://doi.org/10.5555/1953048.2078195.
[22] Abadi M, Agarwal A, Barham P, Brevdo E,
Chen Z, Citro C, et al. Tensorflow: Largescale machine learning on heterogeneous distributed systems. arXiv [Preprint] 2016. Version 2.
https://doi.org/10.48550/arXiv.1603.04467.