Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 942

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Open access
Patrício, Diego Inácio, Rieder, Rafael·2018·Computers and Electronics in Agriculture
DOI 10.1016/j.compag.2018.08.001
Open this paper in CoChat — free account

Abstract

Abstract Grain production plays an important role in the global economy. In this sense, the demand for efficient and safe methods of food production is increasing. Information Technology is one of the tools to that end. Among the available tools, we highlight computer vision solutions combined with artificial intelligence algorithms that achieved important results in the detection of patterns in images. In this context, this work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley. In this sense, we present 25 papers selected in the last five years with different approaches to treat aspects related to disease detection, grain quality, and phenotyping. From the results of the systematic review, it is possible to identify great opportunities, such as the exploitation of GPU (Graphics Processing Unit) and advanced artificial intelligence techniques, such as DBN (Deep Belief Networks) in the construction of robust methods of computer vision applied to precision agriculture.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.compag.2018.08.001

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 6 references

Research works printed but not linked: 56

  1. ACM Digital Library, 2018. <https://dl.acm.org/>.
  2. Barbedo, 2013, Digital image processing techniques for detecting, quantifying and classifying plant diseases, SpringerPlus, 2, 660, 10.1186/2193-1801-2-660 · DOI · Paper page
  3. Barbedo, 2016, A review on the main challenges in automatic plant disease identification based on visible range images, Biosyst. Eng., 144, 52, 10.1016/j.biosystemseng.2016.01.017 · DOI · Paper page
  4. Boniecki, 2014, Detection of the granary weevil based on x-ray images of damaged wheat kernels, J. Stored Prod. Res., 56, 38, 10.1016/j.jspr.2013.11.001 · doi:10.1016/j.jspr.2013.11.001
  5. Breuel, T.M., Ul-Hasan, A., Al-Azawi, M.A., Shafait, F., 2013. High-performance ocr for printed english and fraktur using lstm networks. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 683–687. doi:https://doi.org/10.1109/ICDAR.2013.140. · DOI · Paper page
  6. Chung, 2016, Detecting Bakanae disease in rice seedlings by machine vision, Comput. Electron. Agric., 121, 404, 10.1016/j.compag.2016.01.008 · DOI · Paper page
  7. Coates, A., Baumstarck, P., Le, Q., Ng, A.Y., Scalable learning for object detection with GPU hardware. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 4287–4293. doi:https://doi.org/10.1109/IROS.2009.5354084. <http://ieeexplore.ieee.org/document/5354084/>. · DOI
  8. Davies, E. (Ed.), 2005. Machine Vision, third ed., Signal Processing and its Applications, Morgan Kaufmann, Burlington. doi:https://doi.org/10.1016/B978-0-12-206093-9.50039-3. <http://www.sciencedirect.com/science/article/pii/B9780122060939500393>. · doi:10.1016/b978-0-12-206093-9.50039-3
  9. Ebrahimi, 2014, Toward an automatic wheat purity measuring device: a machine vision-based neural networks-assisted imperialist competitive algorithm approach, Measurement, 55, 196, 10.1016/j.measurement.2014.05.003 · doi:10.1016/j.measurement.2014.05.003
  10. Ferentinos, 2018, Deep learning models for plant disease detection and diagnosis, Comput. Electron. Agric., 145, 311, 10.1016/j.compag.2018.01.009 · DOI · Paper page
  11. Guo, 2015, Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images, Plant Meth., 11, 10.1186/s13007-015-0047-9 · DOI
  12. Han, L., Haleem, M.S., Taylor, M., 2015. A novel computer vision-based approach to automatic detection and severity assessment of crop diseases. In: 2015 Science and Information Conference (SAI), pp. 638–644. doi:https://doi.org/10.1109/SAI.2015.7237209. · DOI
  13. Hornberg, A., 2017. Handbook of Machine and Computer Vision, Wiley-VCH Verlag GmbH & Co. KGaA. doi:https://doi.org/10.1002/9783527413409.fmatter. · DOI
  14. IEEE Xplore Digital Library, 2018. <https://ieeexplore.ieee.org/Xplore/home.jsp>.
  15. Kamilaris, 2018, Deep learning in agriculture: a survey, Comput. Electron. Agric., 147, 70, 10.1016/j.compag.2018.02.016 · DOI · Paper page
  16. Kezhu, 2014, Identification of diseases for soybean seeds by computer vision applying BP neural network, Int. J. Agric. Biol. Eng., 7, 43
  17. Kirk, 2016, Programming Massively Parallel Processors, Third Edition: A Hands-on Approach
  18. Kosov, 2018, Environmental microorganism classification using conditional random fields and deep convolutional neural networks, Pattern Recogn., 77, 248, 10.1016/j.patcog.2017.12.021 · DOI
  19. Kurtulmuş, 2014, Detecting corn tassels using computer vision and support vector machines, Expert Syst. Appl., 41, 7390, 10.1016/j.eswa.2014.06.013 · DOI
  20. Liu, 2016, Detection of aphids in wheat fields using a computer vision technique, Biosyst. Eng., 141, 82, 10.1016/j.biosystemseng.2015.11.005 · DOI
  21. Liu, 2016, A shadow-based method to calculate the percentage of filled rice grains, Biosyst. Eng., 150, 79, 10.1016/j.biosystemseng.2016.07.011 · doi:10.1016/j.biosystemseng.2016.07.011
  22. Lu, 2015, Fine-grained maize tassel trait characterization with multi-view representations, Comput. Electron. Agric., 118, 143, 10.1016/j.compag.2015.08.027 · DOI
  23. Lu, 2016, Region-based colour modelling for joint crop and maize tassel segmentation, Biosyst. Eng., 147, 139, 10.1016/j.biosystemseng.2016.04.007 · DOI
  24. Lu, 2017, An in-field automatic wheat disease diagnosis system, Comput. Electron. Agric., 142, 369, 10.1016/j.compag.2017.09.012 · DOI · Paper page
  25. Mahajan, 2015, Image acquisition techniques for assessment of legume quality, Trends Food Sci. Technol., 42, 116, 10.1016/j.tifs.2015.01.001 · DOI
  26. Marchi, E., Ferroni, G., Eyben, F., Gabrielli, L., Squartini, S., Schuller, B., 2014. Multi-resolution linear prediction based features for audio onset detection with bidirectional lstm neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2164–2168. doi:https://doi.org/10.1109/ICASSP.2014.6853982. · DOI
  27. Naik, 2017, A real-time phenotyping framework using machine learning for plant stress severity rating in soybean, Plant Meth., 13, 23, 10.1186/s13007-017-0173-7 · DOI · Paper page
  28. Olgun, 2016, Wheat grain classification by using dense sift features with svm classifier, Comput. Electron. Agric., 122, 185, 10.1016/j.compag.2016.01.033 · DOI
  29. Pires, 2016, Local descriptors for soybean disease recognition, Comput. Electron. Agric., 125, 48, 10.1016/j.compag.2016.04.032 · DOI
  30. Romualdo, 2014, Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants, Comput. Electron. Agric., 104, 63, 10.1016/j.compag.2014.03.009 · doi:10.1016/j.compag.2014.03.009
  31. Sabanci, 2017, Computer vision-based method for classification of wheat grains using artificial neural network, J. Sci. Food Agric., 97, 2588, 10.1002/jsfa.8080 · DOI
  32. Sadeghi-Tehran, 2017, Automated method to determine two critical growth stages of wheat: heading and flowering, Front. Plant Sci., 8, 10.3389/fpls.2017.00252 · DOI
  33. Sak, H., Senior, A.W., Beaufays, F., 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, CoRR abs/1402.1128. <http://arxiv.org/abs/1402.1128>. · DOI · Paper page
  34. ScienceDirect, 2018. <https://www.sciencedirect.com/>.
  35. Serranti, 2013, The development of a hyperspectral imaging method for the detection of Fusarium-damaged, yellow berry and vitreous Italian durum wheat kernels, Biosyst. Eng., 115, 20, 10.1016/j.biosystemseng.2013.01.011 · doi:10.1016/j.biosystemseng.2013.01.011
  36. Shah, J.P., Prajapati, H.B., Dabhi, V.K., 2016. A survey on detection and classification of rice plant diseases. In: 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1–8. doi:https://doi.org/10.1109/ICCTAC.2016.7567333. · doi:10.1109/icctac.2016.7567333
  37. Shen, 2018, Detection of stored-grain insects using deep learning, Comput. Electron. Agric., 145, 319, 10.1016/j.compag.2017.11.039 · DOI · Paper page
  38. Shrestha, 2016, A two-camera machine vision approach to separating and identifying laboratory sprouted wheat kernels, Biosyst. Eng., 147, 265, 10.1016/j.biosystemseng.2016.04.008 · doi:10.1016/j.biosystemseng.2016.04.008
  39. Shrivastava, 2016, Soybean plant foliar disease detection using image retrieval approaches, Multimedia Tools Appl., 1
  40. Simonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556. <http://arxiv.org/abs/1409.1556>. · arXiv
  41. Singh, 2016, Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition, IET Comput. Vision, 10, 780, 10.1049/iet-cvi.2015.0486 · doi:10.1049/iet-cvi.2015.0486
  42. Springer Link, 2018. <https://link.springer.com/>.
  43. Sun, 2014, Evaluation and analysis the chalkiness of connected rice kernels based on image processing technology and support vector machine, J. Cereal Sci., 60, 426, 10.1016/j.jcs.2014.04.009 · doi:10.1016/j.jcs.2014.04.009
  44. Sun, 2016, Recognition of mould colony on unhulled paddy based on computer vision using conventional machine-learning and deep learning techniques, Sci. Rep., 6, 37994, 10.1038/srep37994 · DOI
  45. Sutskever, I., Vinyals, O., Le, Q.V., 2014. Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems – Volume 2, NIPS’14, MIT Press, Cambridge, MA, USA, pp. 3104–3112. <http://dl.acm.org/citation.cfm?id=2969033.2969173>.
  46. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015, pp. 1–9. doi:https://doi.org/10.1109/CVPR.2015.7298594. · DOI · Paper page
  47. Tang, W., Zhang, Y., Zhang, D., Yang, W., Li, M., 2011. Corn tassel detection based on image processing. In: International Workshop on Image Processing and Optical Engineering, Vol. 8335. doi:https://doi.org/10.1117/12.917672. · doi:10.1117/12.917672
  48. Teixidó, 2012, Definition of linear color models in the rgb vector color space to detect red peaches in orchard images taken under natural illumination, Sensors, 12, 7701, 10.3390/s120607701 · DOI
  49. Visen, 2002, Ae—automation and emerging technologies: specialist neural networks for cereal grain classification, Biosyst. Eng., 82, 151, 10.1006/bioe.2002.0064 · doi:10.1006/bioe.2002.0064
  50. Vithu, 2016, Machine vision system for food grain quality evaluation: A review, Trends Food Sci. Technol., 56, 13, 10.1016/j.tifs.2016.07.011 · doi:10.1016/j.tifs.2016.07.011
  51. Yu, 2013, Automatic image-based detection technology for two critical growth stages of maize: emergence and three-leaf stage, Agric. For. Meteorol., 174, 65, 10.1016/j.agrformet.2013.02.011 · DOI
  52. Zareiforoush, 2015, Potential applications of computer vision in quality inspection of rice: a review, Food Eng. Rev., 7, 321, 10.1007/s12393-014-9101-z · DOI
  53. Zareiforoush, 2015, A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice, Measurement, 66, 26, 10.1016/j.measurement.2015.01.022 · DOI
  54. Zaremba, W., Sutskever, I., Vinyals, O., 2014. Recurrent neural network regularization. <https://arxiv.org/abs/1409.2329>. · arXiv
  55. Zeiler, M.D., Fergus, R., 2013. Visualizing and understanding convolutional networks. CoRR abs/1311.2901. <http://arxiv.org/abs/1311.2901>. · arXiv
  56. Zhu, 2016, In-field automatic observation of wheat heading stage using computer vision, Biosyst. Eng., 143, 28, 10.1016/j.biosystemseng.2015.12.015 · DOI

Cited by

See all 942

Metadata

Authors
Patrício, D. I., & Rieder, R.
Venue
Computers and Electronics in Agriculture
Publisher
Elsevier BV
Published
2018-10
Volume
153
Pages
69-81
ISSN
0168-1699 (ISSN-L)
DOI
10.1016/j.compag.2018.08.001
OpenAlex
W2887311010
Cited by
942
References
6
FWCI
9.50
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. https://app.cochat.ai/papers/10.1016/j.compag.2018.08.001
The paper
Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.08.001

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.