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A review of the use of convolutional neural networks in agriculture

  • A. Kamilaris (a1) and F. X. Prenafeta-Boldú (a2)


Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

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Corresponding author

Author for correspondence: A. Kamilaris, E-mail:


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Abdel-Hamid, O, Mohamed, AR, Jiang, H, Deng, L, Penn, G and Yu, D (2014) Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, 15331545.
Amara, J, Bouaziz, B and Algergawy, A (2017) A deep learning-based approach for banana leaf diseases classification. In Mitschang, B (ed.), Datenbanksysteme für Business, Technologie und Web (BTW 2017) – Workshopband. Lecture Notes in Informatics (LNI). Stuttgart, Germany: Gesellschaft für Informatik, pp. 7988.
Bahrampour, S, Ramakrishnan, N, Schott, L and Shah, M (2015) Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435 [cs.LG].
Bargoti, S and Underwood, J (2017) Deep fruit detection in orchards. In Okamura, A (ed.), 2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ, USA: IEEE, pp. 36263633.
Bastiaanssen, WGM, Molden, DJ and Makin, IW (2000) Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management 46, 137155.
Canziani, A, Paszke, A and Culurciello, E (2016) An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678 [cs.CV].
Carvalho, FP (2006) Agriculture, pesticides, food security and food safety. Environmental Science and Policy 9, 685692.
Chen, SW, Shivakumar, SS, Dcunha, S, Das, J, Okon, E, Qu, C, Taylor, CJ and Kumar, V (2017) Counting apples and oranges with deep learning: a data-driven approach. IEEE Robotics and Automation Letters 2, 781788.
Chen, Y, Lin, Z, Zhao, X, Wang, G and Gu, Y (2014) Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 20942107.
Chi, M, Plaza, A, Benediktsson, JA, Sun, Z, Shen, J and Zhu, Y (2016) Big data for remote sensing: challenges and opportunities. Proceedings of the IEEE 104, 22072219.
Christiansen, P, Nielsen, LN, Steen, KA, Jørgensen, RN and Karstoft, H (2016) Deep anomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16, E1904.
Deng, L and Yu, D (2014) Deep learning: methods and applications. Foundations and Trends in Signal Processing 7(3–4), 197387.
Deng, J, Dong, W, Socher, R, Li, LJ, Li, K and Fei-Fei, L (2009) Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami, FL, USA: IEEE, pp. 248–255.
Douarre, C, Schielein, R, Frindel, C, Gerth, S and Rousseau, D (2016) Deep learning based root-soil segmentation from X-ray tomography. bioRxiv, 071662. doi:
Dyrmann, M, Karstoft, H and Midtiby, HS (2016) Plant species classification using deep convolutional neural network. Biosystems Engineering 151, 7280.
FAO (2009) How to Feed the World in 2050. Rome, Italy: FAO.
Gebbers, R and Adamchuk, VI (2010) Precision agriculture and food security. Science 327, 828831.
Gers, FA, Schmidhuber, J and Cummins, F (2000) Learning to forget: continual prediction with LSTM. Neural Computation 12, 24512471.
Grinblat, GL, Uzal, LC, Larese, MG and Granitto, PM (2016) Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127, 418424.
Hashem, IAT, Yaqoob, I, Anuar, NB, Mokhtar, S, Gani, A and Khan, S (2015) The rise of ‘big data’ on cloud computing: review and open research issues. Information Systems 47, 98115.
Ishimwe, R, Abutaleb, K and Ahmed, F (2014) Applications of thermal imaging in agriculture – a review. Advances in Remote Sensing 3, 128140.
Kamilaris, A and Prenafeta-Boldú, FX (2017) Disaster monitoring using unmanned aerial vehicles and deep learning. In Disaster Management for Resilience and Public Safety Workshop, Proceedings of EnviroInfo 2017. Luxembourg.
Kamilaris, A and Prenafeta-Boldú, FX (2018) Deep learning in agriculture: a survey. Computers and Electronics in Agriculture 147, 7090.
Kamilaris, A, Gao, F, Prenafeta-Boldú, FX and Ali, MI (2016) Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In 3rd World Forum on Internet of Things (WF-IoT). Reston, VA, USA: IEEE, pp. 442–447.
Kamilaris, A, Kartakoullis, A and Prenafeta-Boldú, FX (2017) A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143, 2337.
Karpathy, A, Toderici, G, Shetty, S, Leung, T, Sukthankar, R and Fei-Fei, L (2014) Large-scale video classification with convolutional neural networks. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, pp. 1725–1732.
Kim, Y (2014) Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 1746–1751.
Kitzes, J, Wackernagel, M, Loh, J, Peller, A, Goldfinger, S, Cheng, D and Tea, K (2008) Shrink and share: humanity's present and future ecological footprint. Philosophical Transactions of the Royal Society of London B: Biological Sciences 363(1491), 467475.
Krizhevsky, A, Sutskever, I and Hinton, GE (2012) Imagenet classification with deep convolutional neural networks. In Pereira, F, Burges, CJC, Bottou, L and Weinberger, KQ (eds), Advances in Neural Information Processing Systems 25. Harrahs and Harveys, Lake Tahoe, US: Neural Information Processing Systems Foundation, Inc., pp. 10971105.
Kussul, N, Lavreniuk, M, Skakun, S and Shelestov, A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters 14, 778782.
Kuwata, K and Shibasaki, R (2015) Estimating crop yields with deep learning and remotely sensed data. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy: IEEE, pp. 858–861.
LeCun, Y and Bengio, Y (1995) Convolutional networks for images, speech, and time series. In Arbib, MA (ed.), The Handbook of Brain Theory and Neural Networks. Cambridge, MA, USA: MIT Press, pp. 255258.
LeCun, Y, Bengio, Y and Hinton, G (2015) Deep learning. Nature 521, 436444.
Lee, SH, Chan, CS, Wilkin, P and Remagnino, P (2015) Deep-plant: plant identification with convolutional neural networks. In 2015 IEEE International Conference on Image Processing (ICIP). Piscataway, NJ, USA: IEEE, pp. 452-456.
Liaghat, S and Balasundram, SK (2010) A review: the role of remote sensing in precision agriculture. American Journal of Agricultural and Biological Sciences 5, 5055.
Lu, H, Fu, X, Liu, C, Li, LG, He, YX and Li, NW (2017) Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. Journal of Mountain Science 14, 731741.
Luus, FP, Salmon, BP, van den Bergh, F and Maharaj, BT (2015) Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters 12, 24482452.
Mandic, DP and Chambers, JA (2001) Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. New York, USA: John Wiley.
Mohanty, SP, Hughes, DP and Salathé, M (2016) Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7, 1419. doi: 10.3389/fpls.2016.01419.
Najafabadi, MM, Villanustre, F, Khoshgoftaar, TM, Seliya, N, Wald, R and Muharemagic, E (2015) Deep learning applications and challenges in big data analytics. Journal of Big Data 2, 1.
Oquab, M, Bottou, L, Laptev, I and Sivic, J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA: IEEE, pp. 1717–1724.
Ozdogan, M, Yang, Y, Allez, G and Cervantes, C (2010) Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sensing 2, 22742304.
Pan, SJ and Yang, Q (2010) A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 13451359.
Rahnemoonfar, M and Sheppard, C (2017) Deep count: fruit counting based on deep simulated learning. Sensors 17, 905.
Reyes, AK, Caicedo, JC and Camargo, JE (2015) Fine-tuning deep convolutional networks for plant recognition. In Cappellato, L, Ferro, N, Jones, GJF and San Juan, E (eds), CLEF2015 Working Notes. Working Notes of CLEF 2015 – Conference and Labs of the Evaluation Forum, Toulouse, France, September 8–11, 2015. Toulouse: CLEF. Available online from: (Accessed 11 May 2018).
Sa, I, Ge, Z, Dayoub, F, Upcroft, B, Perez, T and McCool, C (2016) Deepfruits: a fruit detection system using deep neural networks. Sensors 16, E1222.
Santoni, MM, Sensuse, DI, Arymurthy, AM and Fanany, MI (2015) Cattle race classification using gray level co-occurrence matrix convolutional neural networks. Procedia Computer Science 59, 493502.
Saxena, L and Armstrong, L (2014) A survey of image processing techniques for agriculture. In Proceedings of Asian Federation for Information Technology in Agriculture. Perth, Australia: Australian Society of Information and Communication Technologies in Agriculture, pp. 401–413.
Schmidhuber, J (2015) Deep learning in neural networks: an overview. Neural Networks 61, 85117.
Simonyan, K and Zisserman, A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 1409.1556 [cs.CV].
Sladojevic, S, Arsenovic, M, Anderla, A, Culibrk, D and Stefanovic, D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016, 3289801.
Song, X, Zhang, G, Liu, F, Li, D, Zhao, Y and Yang, J (2016) Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land 8, 734748.
Sørensen, RA, Rasmussen, J, Nielsen, J and Jørgensen, RN (2017) Thistle Detection Using Convolutional Neural Networks. Montpellier, France: EFITA Congress.
Steen, KA, Christiansen, P, Karstoft, H and Jørgensen, RN (2016) Using deep learning to challenge safety standard for highly autonomous machines in agriculture. Journal of Imaging 2, 6.
Szegedy, C, Liu, W, Jia, Y, Sermanet, P, Reed, S, Anguelov, D, Erhan, D, Vanhoucke, V and Rabinovich, A (2015) Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, pp. 1–9.
Szegedy, C, Ioffe, S, Vanhoucke, V and Alemi, AA (2017) Inception-v4, Inception-ResNet and the impact of residual connections on learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Palo Alto, CA, USA: AAAI, pp. 4278–4284.
Teke, M, Deveci, HS, Haliloğlu, O, Gürbüz, SZ and Sakarya, U (2013) A short survey of hyperspectral remote sensing applications in agriculture. In Ilarslan, M, Ince, F, Kaynak, O and Basturk, S (eds), 6th International Conference on Recent Advances in Space Technologies (RAST), IEEE. Piscataway, NJ, USA: IEEE, pp. 171176.
Tyagi, AC (2016) Towards a second green revolution. Irrigation and Drainage 65, 388389.
Waga, D and Rabah, K (2014) Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture. World Journal of Computer Application and Technology 2, 7381.
Wan, J, Wang, D, Hoi, SC, Wu, P, Zhu, J, Zhang, Y and Li, J (2014) Deep learning for content-based image retrieval: a comprehensive study. In Proceedings of the 22nd ACM International Conference on Multimedia. New York, USA: ACM, pp. 157–166.
Weber, RH and Weber, R (2010) Internet of Things (Vol. 12). New York, NY, USA: Springer.
Xinshao, W and Cheng, C (2015) Weed seeds classification based on PCANet deep learning baseline. In IEEE Signal and Information Processing Association Annual Summit and Conference (APSIPA). Hong Kong, China: Asia-Pacific Signal and Information Processing Association, pp. 408–415.


A review of the use of convolutional neural networks in agriculture

  • A. Kamilaris (a1) and F. X. Prenafeta-Boldú (a2)


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