Skip to main content
SpringerLink
Log in

Deep Learning Applications in Agriculture: A Short Review

  • Conference paper
  • First Online:
Robot 2019: Fourth Iberian Robotics Conference (ROBOT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1092))

Included in the following conference series:

Abstract

Deep learning (DL) incorporates a modern technique for image processing and big data analysis with large potential. Deep learning is a recent tool in the agricultural domain, being already successfully applied to other domains. This article performs a survey of different deep learning techniques applied to various agricultural problems, such as disease detection/identification, fruit/plants classification and fruit counting among other domains. The paper analyses the specific employed models, the source of the data, the performance of each study, the employed hardware and the possibility of real-time application to study eventual integration with autonomous robotic platforms. The conclusions indicate that deep learning provides high accuracy results, surpassing, with occasional exceptions, alternative traditional image processing techniques in terms of accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Image-net data-set: http://image-net.org/.

  2. 2.

    COCO data-set: http://cocodataset.org/.

  3. 3.

    Review List: https://bit.ly/2ZtS8tA.

References

  1. Atzberger, C.: Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens. 5(2), 949–981 (2013)

    Article  Google Scholar 

  2. Azevedo, F., Shinde, P., Santos, L., Mendes, J., Santos, F.N., Mendonça, H.: Parallelization of a vine trunk detection algorithm for a real time robot localization system. In: 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1–6. IEEE (2019)

    Google Scholar 

  3. Barré, P., Stöver, B.C., Müller, K.F., Steinhage, V.: LeafNet: a computer vision system for automatic plant species identification. Ecol. Inform. 40, 50–56 (2017)

    Article  Google Scholar 

  4. Baweja, H.S., Parhar, T., Mirbod, O., Nuske, S.: Stalknet: a deep learning pipeline for high-throughput measurement of plant stalk count and stalk width. In: Field and Service Robotics, pp. 271–284. Springer (2018)

    Google Scholar 

  5. Cui, H., Zhang, H., Ganger, G.R., Gibbons, P.B., Xing, E.P.: GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server. In: Proceedings of the Eleventh European Conference on Computer Systems, p. 4. ACM (2016)

    Google Scholar 

  6. Deng, L., Yu, D., et al.: Deep learning: methods and applications. Found. Trends® Signal Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  7. Ding, J., Chen, B., Liu, H., Huang, M.: Convolutional neural network with data augmentation for SAR target recognition. IEEE Geosci. Remote Sens. Lett. 13(3), 364–368 (2016)

    Google Scholar 

  8. Douarre, C., Schielein, R., Frindel, C., Gerth, S., Rousseau, D.: Transfer learning from synthetic data applied to soil-root segmentation in X-ray tomography images. J. Imaging 4(5), 65 (2018)

    Article  Google Scholar 

  9. Espejo-Garcia, B., Lopez-Pellicer, F.J., Lacasta, J., Moreno, R.P., Zarazaga-Soria, F.J.: End-to-end sequence labeling via deep learning for automatic extraction of agricultural regulations. Comput. Electron. Agric. 162, 106–111 (2019)

    Article  Google Scholar 

  10. Farooq, A., Hu, J., Jia, X.: Analysis of spectral bands and spatial resolutions for weed classification via deep convolutional neural network. IEEE Geosci. Remote Sens. Lett. 16(2), 183–187 (2018)

    Article  Google Scholar 

  11. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  Google Scholar 

  12. Fuentes, A., Yoon, S., Kim, S., Park, D.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)

    Article  Google Scholar 

  13. Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)

    Article  Google Scholar 

  14. Han, X., Zhong, Y., Cao, L., Zhang, L.: Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens. 9(8), 848 (2017)

    Article  Google Scholar 

  15. Heinrich, K., Roth, A., Breithaupt, L., Möller, B., Maresch, J.: Yield prognosis for the agrarian management of vineyards using deep learning for object counting (2019)

    Google Scholar 

  16. Heo, Y.J., Kim, S.J., Kim, D., Lee, K., Chung, W.K.: Super-high-purity seed sorter using low-latency image-recognition based on deep learning. IEEE Robot. Autom. Lett. 3(4), 3035–3042 (2018)

    Article  Google Scholar 

  17. Kamilaris, A., Prenafeta-Boldú, F.: A review of the use of convolutional neural networks in agriculture. J. Agric. Sci. 156(3), 312–322 (2018)

    Article  Google Scholar 

  18. Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X.: A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23–37 (2017)

    Article  Google Scholar 

  19. Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  20. Kitzes, J., Wackernagel, M., Loh, J., Peller, A., Goldfinger, S., Cheng, D., Tea, K.: Shrink and share: humanity’s present and future ecological footprint. Philos. Trans. Royal Soc. B Biol. Sci. 363(1491), 467–475 (2007)

    Article  Google Scholar 

  21. Koirala, A., Walsh, K., Wang, Z., McCarthy, C.: Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘mangoyolo’. Precis. Agric. 1–29 (2019)

    Google Scholar 

  22. Lammie, C., Olsen, A., Carrick, T., Azghadi, M.R.: Low-power and high-speed deep FPGA inference engines for weed classification at the edge. IEEE Access 7, 51171–51184 (2019)

    Article  Google Scholar 

  23. Lavreniuk, M., Kussul, N., Novikov, A.: Deep learning crop classification approach based on coding input satellite data into the unified hyperspace. In: 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), pp. 239–244. IEEE (2018)

    Google Scholar 

  24. Li, X., Wu, X.: Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4520–4524. IEEE (2015)

    Google Scholar 

  25. Liakos, K., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18(8), 2674 (2018)

    Article  Google Scholar 

  26. Liu, X., Chen, S.W., Aditya, S., Sivakumar, N., Dcunha, S., Qu, C., Taylor, C.J., Das, J., Kumar, V.: Robust fruit counting: Combining deep learning, tracking, and structure from motion. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1045–1052. IEEE (2018)

    Google Scholar 

  27. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  28. Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)

    Article  Google Scholar 

  29. Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., Sun, Z.: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput. Electron. Agric. 154, 18–24 (2018)

    Article  Google Scholar 

  30. McBratney, A., Whelan, B., Ancev, T., Bouma, J.: Future directions of precision agriculture. Precis. Agric. 6(1), 7–23 (2005)

    Article  Google Scholar 

  31. McNicoll, G.: World population ageing 1950–2050. Population Dev. Rev. 28(4), 814–816 (2002)

    Google Scholar 

  32. Mendes, J., Dos Santos, F.N., Ferraz, N., Couto, P., Morais, R.: Vine trunk detector for a reliable robot localization system. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1–6. IEEE (2016)

    Google Scholar 

  33. Patil, K., Kale, N.: A model for smart agriculture using IoT. In: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp. 543–545. IEEE (2016)

    Google Scholar 

  34. Perry, M.: Science and innovation strategic policy plans for the 2020s (EU, AU, UK): will they prepare us for the world in 2050? Appl. Econ. Finance 2(3), 76–84 (2015)

    Article  Google Scholar 

  35. Rançon, F., Bombrun, L., Keresztes, B., Germain, C.: Comparison of sift encoded and deep learning features for the classification and detection of Esca disease in bordeaux vineyards. Remote Sens. 11(1), 1 (2019)

    Article  Google Scholar 

  36. Rivas, A., Chamoso, P., González-Briones, A., Corchado, J.: Detection of cattle using drones and convolutional neural networks. Sensors 18(7), 2048 (2018)

    Article  Google Scholar 

  37. Rußwurm, M., Körner, M.: Multi-temporal land cover classification with long short-term memory neural networks. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. 42, 551 (2017)

    Article  Google Scholar 

  38. Santos, L., Santos, F.N., Magalhães, S., Costa, P., Reis, R.: Path planning approach with the extraction of topological maps from occupancy grid maps in steep slope vineyards. In: 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1–7. IEEE (2019)

    Google Scholar 

  39. Santos, L., Ferraz, N., dos Santos, F.N., Mendes, J., Morais, R., Costa, P., Reis, R.: Path planning aware of soil compaction for steep slope vineyards. In: 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 250–255. IEEE (2018)

    Google Scholar 

  40. dos Santos Ferreira, A., Freitas, D.M., da Silva, G.G., Pistori, H., Folhes, M.T.: Weed detection in soybean crops using convnets. Comput. Electron. Agric. 143, 314–324 (2017)

    Article  Google Scholar 

  41. Saxena, L., Armstrong, L.: A survey of image processing techniques for agriculture (2014)

    Google Scholar 

  42. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015)

    Article  Google Scholar 

  43. Schmitz, A., Moss, C.B.: Mechanized agriculture: machine adoption, farm size, and labor displacement (2015)

    Google Scholar 

  44. Smith, A.G., Petersen, J., Selvan, R., Rasmussen, C.R.: Segmentation of roots in soil with u-net. arXiv preprint arXiv:1902.11050 (2019)

  45. Tseng, D., Wang, D., Chen, C., Miller, L., Song, W., Viers, J., Vougioukas, S., Carpin, S., Ojea, J.A., Goldberg, K.: Towards automating precision irrigation: deep learning to infer local soil moisture conditions from synthetic aerial agricultural images. In: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), pp. 284–291. IEEE (2018)

    Google Scholar 

  46. Uzal, L.C., Grinblat, G.L., Namías, R., Larese, M.G., Bianchi, J., Morandi, E., Granitto, P.M.: Seed-per-pod estimation for plant breeding using deep learning. Comput. Electron. Agric. 150, 196–204 (2018)

    Article  Google Scholar 

  47. Walter, A., Finger, R., Huber, R., Buchmann, N.: Opinion: smart farming is key to developing sustainable agriculture. Proc. Nat. Acad. Sci. 114(24), 6148–6150 (2017)

    Article  Google Scholar 

  48. Wang, C., Gong, L., Yu, Q., Li, X., Xie, Y., Zhou, X.: DLAU: a scalable deep learning accelerator unit on FPGA. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(3), 513–517 (2016)

    Google Scholar 

  49. Yalcin, H., Razavi, S.: Plant classification using convolutional neural networks. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5. IEEE (2016)

    Google Scholar 

  50. Younis, S., Weiland, C., Hoehndorf, R., Dressler, S., Hickler, T., Seeger, B., Schmidt, M.: Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks. Bot. Lett. 165(3–4), 377–383 (2018)

    Article  Google Scholar 

  51. Zhang, X., Qiao, Y., Meng, F., Fan, C., Zhang, M.: Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6, 30370–30377 (2018)

    Article  Google Scholar 

  52. Zhang, Y.D., Dong, Z., Chen, X., Jia, W., Du, S., Muhammad, K., Wang, S.H.: Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools Appl. 78(3), 3613–3632 (2019)

    Article  Google Scholar 

  53. Zhong, L., Hu, L., Zhou, H.: Deep learning based multi-temporal crop classification. Remote Sens. Environ. 221, 430–443 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work is co-financed by the European Regional Development Fund (ERDF) through the Interreg V-A Espanha-Portugal Programme (POCTEP) 2014-2020 within project 0095_BIOTECFOR_1_P. This work also was co-financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project “ROMOVI: POCI-01-0247-FEDER-017945” The opinions included in this paper shall be the sole responsibility of their authors. The European Commission and the Authorities of the Programme aren’t responsible for the use of information contained therein.

Author information

Authors and Affiliations

  1. INESC TEC - INESC Technology and Science, Porto, Portugal

    Luís Santos, Filipe N. Santos, Paulo Moura Oliveira & Pranjali Shinde

  2. UTAD - University of Trás-os-Montes e Alto Douro, Vila Real, Portugal

    Luís Santos & Paulo Moura Oliveira

Authors
  1. Luís Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filipe N. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paulo Moura Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pranjali Shinde
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luís Santos .

Editor information

Editors and Affiliations

  1. School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Manuel F. Silva

  2. Department of Electrical Engineering, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Luís Paulo Reis

  4. UPC, Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  5. Centro Universitario de la Defensa (CUD), Zaragoza, Spain

    Danilo Tardioli

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Santos, L., Santos, F.N., Oliveira, P.M., Shinde, P. (2020). Deep Learning Applications in Agriculture: A Short Review. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_12

Download citation

Publish with us

Policies and ethics

Search

Navigation