Downloaded from https://spj.science.org on July 31, 2023
1. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D,
Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C. Food
security: The challenge of feeding 9 billion people. Science.
2010;327(5967):812–818.
2. Fischer RA, Byerlee D, Edmeades GO. Crop yields and global
food security: Will yield increase continue to feed the world?
Europe Rev Agric Econom. 2015;43(1):191–192.
3. Saito K, Six J, Komatsu S, Snapp S, Rosenstock T, Arouna A,
Cole S, Taulya G, Vanlauwe SB. Agronomic gain: Definition,
approach and applications. Field Crops Res. 2021;270:Article
108193.
4. Bruke M, Lobell DB. Satellite-based assessment of yield
variation and its determinants in smallholder African systems.
Proc Natl Acad Sci U S A. 2017;114(9):2189–2194.
5. Lobell DB, Azzari G, Burke M, Gourlay S, Jin Z, Kilic T,
Murray S. Eyes in the sky, boots on the ground: Assessing satelliteand ground-based approaches to crop yield measurement and
analysis. Amer J Agr Econ. 2019;102(1):202–219.
6. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature.
2015;521:436–444.
7. The scientific events that shaped the decade. Nature.
2019;576:337–338.
8. Popel M, Tomkova M, Tomek J, Kaiser Ł, Uszkoreit J, Bojar O,
Žabokrtský Z. Transforming machine translation: A deep
learning system reaches news translation quality comparable
to human professionals. Nat Commun. 2020;11:Article 4381.
9. Senior W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T,
Qin C, Žídek A, Nelson AWR, Bridgland A, et al. Improved
protein structure prediction using potentials from deep
learning. Nature. 2020;577(7792):706–710.
10. Silver D, Schrittwieser J, Simonyan K, Antonoglou I,
Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, et al.
Mastering the game of go without human knowledge. Nature.
2017;550(7676):354–359.
11. Kamilaris FXP-B, Prenafeta-Boldú FX. Deep learning
in agriculture: A survey. Comput Electron Agric.
2018a;147:70–90.
12. Kamilaris FXP-B, Prenafeta-Boldú FX. A review of the use
of convolutional neural networks in agriculture. J Agric Sci.
2018b;156(3):1–11.
13. Liang W, Zhang H, Zhang G, Cao H. Rice blast disease
recognition using a deep convolutional neural network.
Sci Rep. 2019;9:2869.
14. Sharma P, Berwal YPS, Ghai W. Performance analysis of deep
learning CNN models for disease detection in plants using
image segmentation. Inform Process Agric. 2020;7(4):566–574.
15. Rustia DJA, Chao J-J, Chiu L-Y, Wu Y-F, Chung J-Y, Hsu J-C,
Lin T-T. Automatic greenhouse insect pest detection and
recognition based on a cascaded deep learning classification
method. J Appl Entomol. 2021;145(3):206–222.
16. Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B,
Singh A, Sarkar S. An explainable deep machine vision
framework for plant stress phenotyping. Proc Natl Acad Sci U S A.
2018;115(18):4613–4618.
17. Ma J, Li Y, Chen Y, Du K, Zheng F, Zhang L, Sun Z. Estimating
above ground biomass of winter wheat at early growth stages
using digital images and deep convolutional neural network.
Europe J Agron. 2019;103:117–129.
18. Castro W, Marcato J Jr, Polidoro C, Osco LP, Gonçalves W,
Rodrigues L, Santos M, Jank L, Barrios S, Valle C, et al.
Deep learning applied to phenotyping of biomass in
forages with UAV-based RGB imagery. Sensors (Basel).
2020;20(17):Article 4802.
19. Jin X, Li Z, Feng H, Ren Z, Li S. Deep neural network
algorithm for estimating maize biomass based on simulated
sentinel 2A vegetation indices and leaf area index. The Crop J.
2020;8(1):87–97.
20. Gen L, Che T, Ma M, Tan J, Wang H. Corn biomass estimation
by integrating remote sensing ahd long term observation
data base on machine learning techniques. Remote Sens.
2021;13:Article 2352.
21. Apolo-Apolo OE, Pérez-Ruiz M, Martínez-Guanter J,
Egea GA. Mixed data-based deep neural network to
estimate leaf area index in wheat breeding trials. Agronomy.
2020;10(2):Article 175.
22. Toda Y, Okura F, Ito J, Okada S, Kinoshita T, Tsuji H, Saisho D.
Training instance segmentation neural network with synthetic
datasets for crop seed phenotyping. Commun Biol. 2020;3(1):1–12.
23. Xiong X, Duan L, Liu L, Tu H, Yang P, Wu D, Chen G, Xiong L,
Yang W, Liu Q. Panicle-SEG: A robust image segmentation method
for rice panicles in the field based on deep learning and superpixel
optimization. Plant Methods. 2017;13:Article 104.
24. David E, Madec S, Sadeghi-Tehran P, Aasen H, Zheng B, Liu S,
Kirchgessner N, Ishikawa G, Nagasawa K, Badhon MA, et al.
Global wheat head detection (GWHD) dataset: A large and diverse
dataset of high-resolution RGB-labelled images to develop and
benchmark wheat head detection methods. Plant Phenomics. 2020,
2020;Article 3521852.
25. 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 Res.
2019;235:142–153.
Tanaka et al. 2023 | https://doi.org/10.34133/plantphenomics.0073
34. 35. 36. 37. 38. 39. ground-based sorghum yield estimates in Mali. Remote Sens.
2020;12(1):Article 100.
Zhou X, Zheng HB, Xu XQ, He JY, Ge XK, Yao X, Cheng T,
Zhu Y, Cao WX, Tian YC. Predicting grain yield in rice
using multi-temporal vegetation indices from UAV-based
multispectral and digital imagery. J. Photogramm Remote Sens.
2017;130:246–255.
Wang T, Liu Y, Wang M, Fan Q, Tian H, Qiao X, Li Y.
Application of UAS in crop biomass monitoring: A review.
Front Plant Sci. 2021;12:Article 616689.
Ji Y, Chen Z, Cheng Q, Liu R, Li M, Yan X, Li G, Wang D,
Fu L, Ma Y, et al. Estimation of plant height and yield based
on UAV imagery in faba bean (Vicia faba L.). Plant Methods.
2022;18:Article 26.
Li R, Li M, Ashraf U, Liu S, Zhang J. Exploring the
relationships between yield and yield-related traits for rice
varieties released in China from 1978 to 2017. Front Plant Sci.
2019;10:Article 543.
He K, Zhang X, Ren S, Sun J. Deep residual learning for
image recognition. ArXiv 2015. https://doi.org/10.48550/
arXiv.1512.03385
Lacoste M, Cook S, McNee M, Gale D, Ingram J,
Bellon-Maurel V, MacMillan T, Sylvester-Bradley R,
Kindred D, Bramley R, et al. On-farm experimentation to
transform global agriculture. Nat Food. 2022;3:11–18.
14
Downloaded from https://spj.science.org on July 31, 2023
26. Hang J, Shi L, Yang Q, Chen Z, Yu J, Zha Y. Rice yield
estimation using a CNN-based image-drivin data assimilation
framework. Field Crops Res. 2022;288:Article 108693.
27. GRiSP (Global Rice Science Partnership). Rice almanac. 4th
edition. Los Baños (Philippines): International Rice Research
Institute; 2013.
28. FAO. Guidelines on planning Rice production survey. Rome.
2019.
29. Zeiler MD, Fergus R. Visualizing and understanding
convolutional networks. ArXiv. 2013. https://doi.org/10.48550/
arXiv.1311.2901
30. Lobell DB. The use of satellite data for crop yield gap analysis.
Field Crops Res. 2013;143:56–64.
31. Setiyono TD, Quicho ED, Holecz FH, Khan NI, Romuga G,
Maunahan A, Garcia C, Rala A, Raviz J, Collivignarelli F, et al.
Rice yield estimation using synthetic aperture radar (SAR) and
the ORYZA crop growth model: Development and application
of the system in south and south-east Asian countries. Int J
Remote Sens. 2019;40(21):8093–8124.
32. Jain M, Singh B, Srivastava AAK, Malik RK, McDonald AJ,
Lobell DB. Using satellite data to identify the causes of and
potential solutions for yield gaps in India’s Wheat Belt. Environ
Res Lett. 2017;12(9):Article 094011.
33. Lobell DB, Tommaso SD, You C, Djima IY, Burke M,
Kilic T. Sight for sorghums: Comparisons of satellite- and
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