A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities
Abstract
:1. Introduction
2. Research Method
3. Literature Review
3.1. Deep Learning
3.2. Agriculture before Deep Learning
3.3. Deep Learning Architecture
3.3.1. Convolutional Neural Networks (CNNs)
3.3.2. Recurrent Neural Networks (RNNs)
4. Significance of Deep Learning in Agriculture
4.1. Counting of Fruit
4.2. Management of Water
4.3. Crop Management
4.4. Soil Management
4.5. Weed Detection
4.6. Seed Classification
4.7. Classification of Plant Diseases
4.8. Yield Prediction
4.9. Disease Detection
5. Application of Deep-Learning Models in Agriculture
6. Results and Discussions
7. Future Challenges and Opportunities in the Agricultural Domain
8. Conclusions and Future Work
Funding
Data Availability Statement
Conflicts of Interest
References
- Santos, L.; Santos, F.; Mendes, J.; Costa, P.; Lima, J.; Reis, R.; Shinde, P. Path planning aware of robot’s center of mass for steep slope vineyards. Robotica 2020, 38, 684–698. [Google Scholar] [CrossRef]
- Patil, K.A.; Kale, N.R. A model for smart agriculture using IoT. In Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, 22–24 December 2016; pp. 543–545. [Google Scholar]
- Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens. 2013, 5, 949–981. [Google Scholar] [CrossRef] [Green Version]
- 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 Proceedings of the 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, Portugal, 25–27 April 2018. [Google Scholar] [CrossRef]
- Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture 2022, 12, 1745. [Google Scholar] [CrossRef]
- Walter, A.; Finger, R.; Huber, R.; Buchmann, N. Opinion: Smart farming is key to developing sustainable agriculture. Proc. Natl. Acad. Sci. USA 2017, 114, 6148–6150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kamilaris, A.; Prenafeta-Boldú, F.X. A review of the use of convolutional neural networks in agriculture. J. Agric. Sci. 2018, 156, 312–322. [Google Scholar] [CrossRef] [Green Version]
- Bouguettaya, A.; Zarzour, H.; Kechida, A.; Taberkit, A.M. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. In Cluster Computing; Springer Science and Business Media LLC: Berlin/Heidelberg, Germany, 2022. [Google Scholar] [CrossRef]
- Khan, A.; Vibhute, A.D.; Mali, S.; Patil, C.H. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. In Ecological Informatics; Elsevier BV: Amsterdam, The Netherlands, 2022; Volume 69, p. 101678. [Google Scholar]
- Kashyap, P. Machine Learning for Decision-Makers: Cognitive Computing Fundamentals for Better Decision Making; Apress: Bangalore, India, 2017; pp. 227–228. [Google Scholar]
- Magomadov, V.S. Deep Plearning and its role in smart agriculture. J. Phys. Conf. Ser. 2019, 1399, 044109. [Google Scholar] [CrossRef]
- Graves, A.; Schmidhuber, J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 2005, 18, 602–610. [Google Scholar] [CrossRef]
- Jain, A.; Zamir, A.R.; Savarese, S.; Saxena, A. Structural-run: Deep learning on Spatio-temporal graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 5308–5317. [Google Scholar]
- Ren, C.; Kim, D.K.; Jeong, D. A survey of deep learning in agriculture: Techniques and their applications. J. Inf. Process. Syst. 2020, 16, 1015–1033. [Google Scholar]
- Santos, L.; Santos, F.N.; Oliveira, P.M.; Shinde, P. Deep learning applications in agriculture: A short review. In Proceedings of the Fourth Iberian Robotics Conference: Advances in Robotics, Robot 2019: Porto, Portugal, 20–22 November 2019; Silva, M.F., Lima, J.L., Reis, L.P., Sanfeliu, A., Tardioli, D., Eds.; Springer: Cham, Switzerland; pp. 139–151. Available online: https://doi.org/10.1007/978-3-030-35990-4_12 (accessed on 23 February 2022). [CrossRef]
- Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018, 147, 70–90. Available online: https://doi.org/10.1016/j.compag.2018.02.016 (accessed on 23 February 2022). [CrossRef] [Green Version]
- Thai-Nghe, N.; Tri, N.T.; Hoa, N.H. Deep Learning for Rice Leaf Disease Detection in Smart Agriculture. In Artificial Intelligence in Data and Big Data Processing; Springer International Publishing: Berlin/Heidelberg, Germany, 2022; pp. 659–670. [Google Scholar]
- Traditional Agriculture: An Efficient and Sustainable Farming Method. Stories.pinduoduo-global.com. 2021. Available online: https://stories.pinduoduo-global.com/agritech-hub/traditionalagriculture#:~:text=Traditional%20agriculture%20can%20be%20defined,cultural%20beliefs%20of%20the%20farmers (accessed on 23 February 2022).
- Shaila, M.; Begum, N. Ancient farming methods of seed storage and pest management practices in India—A review. Plant Arch. 2021, 21, 499–509. [Google Scholar]
- Dargan, S.; Kumar, M.; Ayyagari, M.R.; Kumar, G. A survey of deep learning and its applications: A new paradigm to machine learning. Arch. Comput. Methods Eng. 2020, 27, 1071–1092. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Abdullahi, H.S.; Sheriff, R.; Mahieddine, F. Convolution neural network in precision agriculture for plant image recognition and classification. In 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, UK, 16–18 August 2017; IEEE: Piscataway, NJ, USA, 2017; Volume 10, pp. 256–272. [Google Scholar]
- Ajit, A.; Acharya, K.; Samanta, A. A review of convolutional neural networks. In Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 24–25 February 2020. [Google Scholar]
- Zaremba, W.; Sutskever, I.; Vinyals, O. Recurrent neural network regularization. arXiv 2014, arXiv:1409.2329. in press. [Google Scholar]
- Chen, S.W.; Shivakumar, S.S.; Dcunha, S.; Das, J.; Okon, E.; Qu, C.; Taylor, C.J.; Kumar, V. Counting apples and oranges with deep learning: A data-driven approach. IEEE Robot. Autom. Lett. 2017, 2, 781–788. [Google Scholar] [CrossRef]
- Rahnemoonfar, M.; Sheppard, C. Deep count: Fruit counting based on deep simulated learning. Sensors 2017, 17, 905. [Google Scholar] [CrossRef] [Green Version]
- Apolo-Apolo, O.E.; Martínez-Guanter, J.; Egea, G.; Raja, P.; Pérez-Ruiz, M. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur. J. Agron. 2020, 115, 126030. [Google Scholar] [CrossRef]
- Sa, I.; Ge, Z.; Dayoub, F.; Upcroft, B.; Perez, T.; McCool, C. Deepfruits: A fruit detection system using deep neural networks. Sensors 2016, 16, 1222. [Google Scholar] [CrossRef] [Green Version]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Proceedings of the Neural Information Processing Systems Conference, Lake Tahoe, NV, USA, 3–6 December 2012; Neural Information Processing Systems Foundation, Inc.: Ljubljana, Slovenia, 2012; pp. 1097–1105. [Google Scholar]
- Bargoti, S.; Underwood, J. Deep fruit detection in orchards. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May–3 June 2017. [Google Scholar]
- Fu, L.; Feng, Y.; Elkamil, T.; Liu, Z.; Li, R.; Cui, Y. Image recognition method of multi-cluster kiwifruit in field based on convolutional neural networks. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 2018, 34, 205–211. [Google Scholar]
- Katarzyna, R.; Paweł, M. A vision-based method utilizing deep convolutional neural networks for fruit variety classification in uncertainty conditions of retail sales. Appl. Sci. 2019, 9, 3971. [Google Scholar] [CrossRef] [Green Version]
- Villacrés, J.F.; Auat Cheein, F. Detection and characterization of cherries: A deep learning usability case study in Chile. Agronomy 2020, 10, 835. [Google Scholar] [CrossRef]
- Chung, D.T.P.; Van Tai, D. A fruits recognition system based on a modern deep learning technique. J. Phys. Conf. Ser. 2019, 1327, 012050. [Google Scholar] [CrossRef]
- Wang, S.H.; Chen, Y. Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique. Multim. Tools Appl. 2018, 79, 15117–15133. [Google Scholar] [CrossRef]
- Kestur, R.; Meduri, A.; Narasipura, O. MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard. Eng. Appl. Artif. Intell. 2019, 77, 59–69. [Google Scholar] [CrossRef]
- Santos, T.T.; de Souza, L.L.; dos Santos, A.A.; Avila, S. Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Comput. Electron. Agric. 2020, 170, 105247. [Google Scholar] [CrossRef] [Green Version]
- Chen, H.; Chen, A.; Xu, L.; Xie, H.; Qiao, H.; Lin, Q.; Cai, K. A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric. Water Manag. 2020, 240, 106303. [Google Scholar] [CrossRef]
- Garg, D.; Khan, S.; Alam, M. Integrative use of IoT and deep learning for agricultural applications. In Proceedings of ICETIT 2019: Emerging Trends in Information Technology; Springer: Cham, Switzerland, 2020; pp. 521–531. [Google Scholar]
- Mohan, P.; Patil, K.K. Deep learning based weighted SOM to forecast weather and crop prediction for agriculture application. Int. J. Intell. Eng. Syst. 2018, 11, 167–176. [Google Scholar] [CrossRef]
- Yang, X.; Sun, M. A survey on deep learning in crop planting. IOP Conf. Ser. Mater. Sci. Eng. 2019, 490, 062053. [Google Scholar] [CrossRef]
- Dharani, M.K.; Thamilselvan, R.; Natesan, P.; Kalaivaani, P.C.D.; Santhoshkumar, S. Review Pon crop prediction using deep learning techniques. J. Phys. Conf. Ser. 2021, 1767, 012026. [Google Scholar] [CrossRef]
- Zheng, Y.Y.; Kong, J.L.; Jin, X.B.; Wang, X.Y.; Su, T.L.; Zuo, M. CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors 2019, 19, 1058. [Google Scholar] [CrossRef] [Green Version]
- Lottes, P.; Behley, J.; Chebrolu, N.; Milioto, A.; Stachniss, C. Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. J. Field Robot. 2020, 37, 20–34. [Google Scholar] [CrossRef]
- Lottes, P.; Behley, J.; Milioto, A.; Stachniss, C. Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. IEEE Robot. Autom. Lett. 2018, 3, 2870–2877. [Google Scholar] [CrossRef] [Green Version]
- Chavan, T.R.; Nandedkar, A.V. AgroAVNET for crops and weeds classification: A step forward in automatic farming. Comput. Electron. Agric. 2018, 154, 361–372. [Google Scholar] [CrossRef]
- Suh, H.K.; Ijsselmuiden, J.; Hofstee, J.W.; van Henten, E.J. Transfer learning for the classification of sugar beet and volunteer potato under field conditions. Biosyst. Eng. 2018, 174, 50–65. [Google Scholar] [CrossRef]
- Meng, S.; Wang, X.; Hu, X.; Luo, C.; Zhong, Y. Deep learning-based crop mapping in the cloudy season using one-shot hyperspectral satellite imagery. Comput. Electron. Agric. 2021, 186, 106188. [Google Scholar] [CrossRef]
- Cai, Y.; Zheng, W.; Zhang, X.; Zhangzhong, L.; Xue, X. Research on soil moisture prediction model based on deep learning. PLoS ONE 2019, 14, e0214508. [Google Scholar] [CrossRef]
- Yashwanth, M.; Chandra, M.L.; Pallavi, K.; Showkat, D.; Kumar, P.S. Agriculture automation using deep learning methods implemented using keras. In Proceedings of the 2020 IEEE International Conference for Innovation in Technology (INOCON), Bangalore, India, 6–8 November 2020. [Google Scholar]
- 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 Proceedings of the 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 20–24 August 2018; pp. 284–291. [Google Scholar]
- Westwood, J.H.; Charudattan, R.; Duke, S.O.; Fennimore, S.A.; Marrone, P.; Slaughter, D.C.; Swanton, C.; Zollinger, R. Weed management in 2050: Perspectives on the future of weed science. Weed Sci. 2018, 66, 275–285. [Google Scholar] [CrossRef] [Green Version]
- Mishra, A.M.; Gautam, V. Weed species identification in different crops using precision weed management: A review. Proc. CEUR Workshop 2021, 180–194. Available online: https://www.semanticscholar.org/paper/Weed-Species-Identification-in-Different-Crops-Weed-Mishra-Gautam/8710e1a04eada39809b159ea8650f4c639c9bf19 (accessed on 3 July 2022).
- Gulzar, Y.; Hamid, Y.; Soomro, A.B.; Alwan, A.A.; Journaux, L. A convolution neural network-based seed classification system. Symmetry 2020, 12, 2018. [Google Scholar] [CrossRef]
- Sharma, A.; Jain, A.; Gupta, P.; Chowdary, V. Machine learning applications for precision agriculture: A comprehensive review. IEEE Access 2020, 9, 4843–4873. [Google Scholar] [CrossRef]
- Saleem, M.H.; Potgieter, J.; Arif, K.M. Plant disease detection and classification by deep learning. Plants 2019, 8, 468. [Google Scholar] [CrossRef] [Green Version]
- Amara, J.; Bouaziz, B.; Algergawy, A. A deep learning-based approach for banana leaf diseases classification. In Proceedings of the Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband, Stuttgart, Germany, 6–7 March 2017; Gesellschaft für Informatik e.V.: Bonn, Germany, 2017; pp. 79–88. [Google Scholar]
- Dipali, M.; Deepa, D. Automation, and integration of growth monitoring in plants (with disease prediction) and crop prediction. Mater. Today Proc. 2021, 43, 3922–3927. [Google Scholar]
- Akash, S.; Malik, A. A hybrid model for the classification of sunflower diseases using deep learning. In Proceedings of the 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, UK, 28–30 April 2021; pp. 58–62. [Google Scholar]
- Ahmed, A.A.; Reddy, G.H. A mobile-based system for detecting plant leaf diseases using deep learning. AgriEngineering 2021, 3, 478–493. [Google Scholar] [CrossRef]
- Pallagani, V.; Khandelwal, V.; Chandra, B.; Udutalapally, V.; Das, D.; Mohanty, S.P. DCrop: A deep-learning-based framework for accurate prediction of diseases of crops in smart agriculture. In Proceedings of the 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), Rourkela, India, 16–18 December 2019; pp. 29–33. [Google Scholar]
- Sladojevic, S.; Arsenovic, M.; Anderla, A.; Culibrk, D.; Stefanovic, D. Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 2016, 3289801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Arivazhagan, S.; Shebiah, R.N.; Ananthi, S.; Varthini, S.V. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 2013, 15, 211–217. [Google Scholar]
- Chen, J.; Chen, J.; Zhang, D.; Sun, Y.; Nanehkaran, Y.A. Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agric. 2020, 173, 105393. [Google Scholar] [CrossRef]
- Sharma, P.; Berwal, Y.P.S.; Ghai, W. Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inf. Process. Agric. 2020, 7, 566–574. [Google Scholar] [CrossRef]
- Atila, Ü.; Uçar, M.; Akyol, K.; Uçar, E. Plant leaf disease classification using EfficientNet deep learning model. Ecol. Inform. 2021, 61, 101182. [Google Scholar] [CrossRef]
- Kaur, P.; Harnal, S.; Tiwari, R.; Upadhyay, S.; Bhatia, S.; Mashat, A.; Alabdali, A.M. Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors 2022, 22, 575. [Google Scholar] [CrossRef]
- Ji, M.; Zhang, L.; Wu, Q. Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf. Process. Agric. 2020, 7, 418–426. [Google Scholar] [CrossRef]
- Gadekallu, T.R.; Rajput, D.S.; Reddy, M.P.K.; Lakshmanna, K.; Bhattacharya, S.; Singh, S.; Jolfaei, A.; Alazab, M. A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J. Real-Time Image Process. 2021, 18, 1383–1396. [Google Scholar] [CrossRef]
- Azimi, S.; Kaur, T.; Gandhi, T.K. A deep learning approach to measure stress level in plants due to nitrogen deficiency. Measurement 2021, 173, 108650. [Google Scholar] [CrossRef]
- Joshi, R.C.; Kaushik, M.; Dutta, M.K.; Srivastava, A.; Choudhary, N. VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecol. Inform. 2021, 61, 101197. [Google Scholar] [CrossRef]
- Kavitha, A. Deep Learning for Smart Agriculture. Int. J. Eng. Res. Technol. 2021, 9. Available online: https://www.semanticscholar.org/paper/Deep-Learning-for-Smart-Agriculture-Kavitha/0a272722fe4838cce5af0bb907310bf76927406d (accessed on 3 July 2022).
- Josephine, B.; Ramya, K.; Rao, K.V.S.N.; Kuchibhotla, P.; Venkata, K.; Rahamathulla, S. Crop yield prediction using machine learning. Int. J. Sci. Technol. Res. 2020, 9. Available online: http://www.ijstr.org/paper-references.php?ref=IJSTR-0120-29576 (accessed on 3 July 2022).
- Khaki, S.; Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci. 2019, 10, 621. Available online: https://doi.org/10.3389/fpls.2019.00621 (accessed on 23 February 2022). [CrossRef] [Green Version]
- Deepika, P.; Kaliraj, S. A survey on pest and disease monitoring of crops. In Proceedings of the 2021 3rd International Conference on Signal Processing and Communication (ICPSC), Coimbatore, India, 13–14 May 2021; Available online: https://doi.org/10.1109/icspc51351.2021.9451787 (accessed on 23 February 2022).
- Ale, L.; Sheta, A.; Li, L.; Wang, Y.; Zhang, N. Deep learning based plant disease detection for smart agriculture. In Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA, 9–13 December 2019; Available online: https://doi.org/10.1109/gcwkshps45667.2019.9024439 (accessed on 23 February 2022).
- Zhu, N.; Liu, X.; Liu, Z.; Hu, K.; Wang, Y.; Tan, J.; Huang, M.; Zhu, Q.; Ji, X.; Jiang, Y.; et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int. J. Agric. Biol. Eng. 2018, 11, 32–44. [Google Scholar] [CrossRef]
- Deep Learning for Image-Based Plant Disease Detection. Let’s Nurture—An IT Company Nurturing Ideas into Reality. 2022. Available online: https://www.letsnurture.com/blog/using-deep-learning-for-image-based-plant-disease-detection.html (accessed on 23 February 2022).
- Zhao, Q.; Koch, C. Learning saliency-based visual attention: A Review. Signal Process. 2013, 93, 1401–1407. [Google Scholar] [CrossRef]
- Kümmerer, M.; Theis, L.; Bethge, M. Deep Gaze I: Boosting saliency prediction with feature maps trained on ImageNet. In Proceedings of the 2015 International Conference on Learning Representations (ICLR), San Diego, CA, USA, 8 May 2014; Available online: https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_2160776 (accessed on 24 February 2022).
- Shrikumar, A.; Greenside, P.; Kundaje, A. Reverse-complement parameter sharing improves deep learning models for genomics. bioRxiv 2017, in press.
- Springenberg, J.T.; Dosovitskiy, A.; Brox, T.; Riedmiller, M. Striving for simplicity: The all convolutional net. arXiv. 2015. in press. Available online: https://arxiv.org/abs/1412.6806 (accessed on 24 February 2022).
- Zeiler, M.D.; Fergus, R. Visualizing and understanding Convolutional Networks. In Proceedings of Computer Vision–ECCV 2014: 13th European Conference, Part I 13, Zurich, Switzerland, 6–12 September 2014; Springer International Publishing: Cham, Switzerland, 2014; pp. 818–833. [Google Scholar]
- Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning deep features for discriminative localization. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016. [Google Scholar]
- Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.-R.; Samek, W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 2015, 10, e0130140. [Google Scholar] [CrossRef] [Green Version]
- Uzal, L.C.; Grinblat, G.L.; Namías, R.; Larese, M.G.; Bianchi, J.S.; Morandi, E.N.; Granitto, P.M. Seed-per-pod estimation for plant breeding using deep learning. Comput. Electron. Agric. 2018, 150, 196–204. [Google Scholar] [CrossRef]
- Nkemelu, D.K.; Omeiza, D.; Lubalo, N. Deep convolutional neural network for plant seedlings classification. arXiv. 2018. in press. Available online: https://arxiv.org/abs/1811.08404?source=post_page (accessed on 24 February 2022).
- Amiryousefi, M.R.; Mohebbi, M.; Tehranifar, A. Pomegranate seed clustering by machine vision. Food Sci. Nutr. 2017, 6, 18–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sujatha, R.; Chatterjee, J.M.; Jhanjhi, N.Z.; Brohi, S.N. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess. Microsyst. 2021, 80, 103615. [Google Scholar] [CrossRef]
- Arnal Barbedo, J.G. Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 2019, 180, 96–107. [Google Scholar] [CrossRef]
- Liu, P.; Mahmood, T.; Khan, Q. Multi-attribute decision-making based on prioritized aggregation operator under hesitant intuitionistic fuzzy linguistic environment. Symmetry 2017, 9, 270. [Google Scholar] [CrossRef] [Green Version]
- Bresilla, K.; Perulli, G.D.; Boini, A.; Morandi, B.; Grappadelli, L.; Manfrini, L. Single-shot convolution neural networks for real-time fruit detection within the tree. Front. Plant Sci. 2019, 10, 611. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Altaheri, H.; Alsulaiman, M.; Muhammad, G. Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access 2019, 7, 117115–117133. [Google Scholar] [CrossRef]
- Meshram, V.; Patil, K.; Meshram, V.; Hanchate, D.; Ramkteke, S.D. Machine learning in agriculture domain: A state-of-art survey. Artif. Intell. Life Sci. 2021, 1, 100010. [Google Scholar] [CrossRef]
- Lu, S.; Chen, W.; Zhang, X.; Karkee, M. Canopy-attention-YOLOv4-based immature/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation. Comput. Electron. Agric. 2022, 193, 106696. [Google Scholar] [CrossRef]
- Lyu, S.; Li, R.; Zhao, Y.; Li, Z.; Fan, R.; Liu, S. Green citrus detection and counting in orchards based on YOLOv5-CS and AI edge system. Sensors 2022, 22, 576. [Google Scholar] [CrossRef]
- Khan, R.; Dhingra, N.; Bhati, N. Role of Artificial Intelligence in Agriculture: A Comparative Study. In Transforming Management with AI, Big-Data, and IoT; Springer International Publishing: Cham, Switzerland, 2022; pp. 73–83. [Google Scholar]
- Wang, C.; Liu, B.; Liu, L.; Zhu, Y.; Hou, J.; Liu, P.; Li, X. A review of deep learning used in the hyperspectral image analysis for agriculture. Artif. Intell. Rev. 2021, 54, 5205–5253. [Google Scholar] [CrossRef]
- Coulibaly, S.; Kamsu-Foguem, B.; Kamissoko, D.; Traore, D. Deep neural networks with transfer learning in Millet crop images. Comput. Ind. 2019, 108, 115–120. [Google Scholar] [CrossRef] [Green Version]
- Sahni, V.; Srivastava, S.; Khan, R. Modelling techniques to improve the quality of food using artificial intelligence. J. Food Qual. 2021, 2021, 2140010. [Google Scholar] [CrossRef]
- Khan, R.; Tyagi, N.; Chauhan, N. Safety of food and food warehouse using VIBHISHAN. J. Food Qual. 2021, 2021, 1328332. [Google Scholar] [CrossRef]
- Kaya, A.; Keceli, A.S.; Catal, C.; Yalic, H.Y.; Temucin, H.; Tekinerdogan, B. Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 2019, 158, 20–29. [Google Scholar] [CrossRef]
- Sharma, M.; Nath, K.; Sharma, R.K.; Kumar, C.J.; Chaudhary, A. Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant. Electronics 2022, 11, 148. [Google Scholar] [CrossRef]
- Argüeso, D.; Picon, A.; Irusta, U.; Medela, A.; San-Emeterio, M.G.; Bereciartua, A.; Alvarez-Gila, A. Few-shot learning approach for plant disease classification using images taken in the field. Comput. Electron. Agric. 2020, 175, 105542. [Google Scholar] [CrossRef]
- Zhong, F.M.; Chen, Z.K.; Zhang, Y.C.; Xia, F. Zero-and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders. Comput. Electron. Agric. 2020, 179, 105828. [Google Scholar] [CrossRef]
- Jiang, H.H.; Zhang, C.Y.; Qiao, Y.L.; Zhang, Z.; Zhang, W.J.; Song, C.Q. CNN feature-based graph convolutional network for weed and crop recognition in smart farming. Comput. Electron. Agric. 2020, 174, 105450. [Google Scholar] [CrossRef]
- Khaki, S.; Pham, H.; Han, Y.; Kuhl, A.; Kent, W.; Wang, L. Deepcorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation. Knowl. Based Syst. 2021, 218, 106874. [Google Scholar] [CrossRef]
- Sa, I.; Chen, Z.; Popović, M.; Khanna, R.; Liebisch, F.; Nieto, J.; Siegwart, R. WeedNet: Dense semantic weed classification using multispectral images and MAV for smart farming. IEEE Robot. Autom. Lett. 2017, 3, 588–595. [Google Scholar] [CrossRef] [Green Version]
- Sa, I.; Popović, M.; Khanna, R.; Chen, Z.; Lottes, P.; Liebisch, F.; Nieto, J.; Stachniss, C.; Walter, A.; Siegwart, R. WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens. 2018, 10, 1423. [Google Scholar] [CrossRef] [Green Version]
- Jiao, L.; Dong, S.; Zhang, S.; Xie, C.; Wang, H. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Comput. Electron. Agric. 2020, 174, 105522. [Google Scholar] [CrossRef]
- Eunice, J.; Popescu, D.E.; Chowdary, M.K.; Hemanth, J. Deep learning-based leaf disease detection in crops using images for agricultural applications. Agronomy 2022, 12, 2395. [Google Scholar]
- Wu, Q.; Chen, Y.; Meng, J. DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access 2020, 8, 98716–98728. [Google Scholar] [CrossRef]
- Hammouch, H.; El-Yacoubi, M.; Qin, H.; Berrahou, A.; Berbia, H.; Chikhaoui, M. A two-stage deep convolutional generative adversarial network-based data augmentation scheme for agriculture image regression tasks. In Proceedings of the 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), Beijing, China, 18–20 December 2021. [Google Scholar]
- Sourav, A.I.; Emanuel, A.W.R. Recent Trends of Big Data in Precision Agriculture: A Review. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2021; Volume 1096, p. 012081. [Google Scholar]
- Bharman, P.; Ahmad Saad, S.; Khan, S.; Jahan, I.; Ray, M.; Biswas, M. Deep Learning in Agriculture: A Review. Asian J. Res. Comput. Sci. 2022, 13, 28–47. [Google Scholar] [CrossRef]
- Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2014. [Google Scholar]
- Liu, J.; Cheng, L.; Yan, S. Deep learning with noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 16–20 June 2019. [Google Scholar]
- Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; Vladu, A. Towards deep learning models resistant to adversarial attacks. arXiv 2018, arXiv:1706.06083. in press. [Google Scholar]
- Finn, C.; Abbeel, P.; Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6–11 August 2017; Volume 70, pp. 1126–1135. [Google Scholar]
- Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco CA, USA, 13–17 August 2016; Association for Computing Machinery: New York, NY, USA, 2016; pp. 1135–1144. [Google Scholar]
- Lundberg, S.M.; Lee, S.I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 2017, 30, 4765–4774. [Google Scholar]
- Caruana, R. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 10–13 August 2015; pp. 1721–1730. [Google Scholar]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30, 5998–6008. [Google Scholar]
- Snell, J.; Swersky, K.; Zemel, R. Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. 2017, 30, 4077–4087. [Google Scholar]
Ref | DL model | Dataset | Accuracy |
---|---|---|---|
[29] | Faster R-CNN | TL + field farm | 0.83 F1-score |
[30] | Inception-ResNet-v4 | ILSVRC-2010&2012 | N/A |
[31] | VGG-16 | Orchard | 95% |
[32] | CNN | Kiwifruit | 89.29% |
[33] | YOLO V3 | PT + WGISD | — |
[34] | Faster R-CNN + Iv2 | Cherries | 85% |
[35] | E-Net | Fruit 360 | 93.7% |
[36] | 8-layer CNN model | Self collecting | 95.67% |
[37] | M-Net | Mango orchard | 73.6% |
[38] | YOLO V3 | PT + WGISD | 97.3% for test |
Ref | DL Model | Application | Accuracy |
---|---|---|---|
[45,46] | FCN architecture/Stem-seg-S | Joint stem detection and crop/weed classification | mAP, 85.4% (stem detection) 69.7% (segmentation) |
[47] | AgroAVNET | Crop/weed classification | 98.23% |
[48] | AlexNet, VGG-19, GoogLeNet, ResNet-50, ResNet-101, Inception-v3 | Crop/weed classification | 96% (VGG-19) |
[49] | 1D/2D/3D CNN | Crop mapping | 94% (3D CNN) |
Ref. | Leaf Type | Method | Accuracy |
---|---|---|---|
[65] | Rice | VGGNet | 92.00 |
[66] | Tomato | S-CNN and F-CNN | 98.30 |
[67] | Plant leaf | EfficientNet | 96.18 |
[68] | Grape | Hy-CNN | 98.70 |
[69] | Grape | United model | 98.20 |
[70] | Plant leaf | Whale and DL | 95.10 |
[71] | Crop | FCNN and SCNN | 92.01 |
[72] | Coffee | Deep CNN | 98.00 |
Ref | Method Used | Purpose of Employing Method | Key Insights |
---|---|---|---|
[20] | Automated fruit detection and algorithms using a DL algorithm pipeline consisting of part 0, part 1, part 2, and part 3 | Counting fruit | Optimization of agriculture production Promising harvesting results |
[27] | Inception-ResNet | Counting fruit | Provides high accuracy in the counting of fruit Uses synthetic images to test authentic images, achieving a 91% accuracy rate |
[26] | Near-infrared (NIR) spectroscopy | Management of water | Increased water protection and recycling Provides information that helps make effective decisions in water management |
[41] | Evapotranspiration | Management of water | Allows the prediction of water specifications for real-time irrigation management |
[42] | R-CNN for counting and measuring crop plantings | Crop management | The CNN helps in identifying localized features of roots and shoots CGG-16 allows categorization of crops and weeds |
[43] | Two-layered DNN LSTM | Crop management | Highlights soil and environmental characteristics Prominent vegetative index used to create estimations of crop production for tomato, soybean, and corn |
[51] | Keras API through Python | Soil management | Helps in preventing the harmful effects of herbicides and toxicity in soil and in retaining moisture |
[52] | First-order agriculture simulator using discrete time | Soil management | Improves aerial images and provides soil moisture information |
[51] | First-order agriculture simulation with Richard equation | Weed detection | Increases protection of soil from toxicity and ensures plants achieve good production yields |
[54] | SVM and CNN | Weed detection | The camera is used to take an image of a weed, and then the gray-level occurrence matrix is employed to determine the homogeneity among the images Reduces burden on farmers |
[55] | CNN | Seed classification | Increased efficiency in seed classification |
[74] | Random forest (RF) | Yield prediction | Provides the best yield prediction accuracy |
[76] | Histogram regression | Yield prediction | Offers accuracy in the determination of soybean varieties |
[78] | CNN | Disease detection | Achieved an 89% accuracy rate when compared to other traditional crop disease detection methods Improves pest control and makes robotic harvesting possible with increased yield prediction and disaster monitoring abilities for crops |
[95] | Bio-inspired methods | Harvesting | Increases harvesting efficiency Improves accuracy for harvesting in agriculture |
[96] | Canopy-attention-YOLOv4 | Fruit detection | Precision = 94.89% Recall = 90.08% F1 = 92.52% |
[97] | YOLOv5-CS (citrus sort) | Fruit detection and counting | Recall = 97.66% Precision = 86.97% mAP = 98.23% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Albahar, M. A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities. Agriculture 2023, 13, 540. https://doi.org/10.3390/agriculture13030540
Albahar M. A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities. Agriculture. 2023; 13(3):540. https://doi.org/10.3390/agriculture13030540
Chicago/Turabian StyleAlbahar, Marwan. 2023. "A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities" Agriculture 13, no. 3: 540. https://doi.org/10.3390/agriculture13030540