Research Progress in Detection and Identification of Crop Diseases and Insect Pests Based on Deep Learning
College of Mechanical and Transportation, Southwest Forestry University, Kunming 650224, China
| Abstract: | Crops play a vital role in the global food supply. Various environmental factors can cause crop diseases and insect pests, resulting in severe production losses. However, manual detection of crop diseases and insect pests is a time-consuming and error-prone process, which often fails to identify and prevent the spread of plant diseases in a timely and effective manner. To overcome these challenges, machine learning (ML) and deep learning (DL) technologies have been introduced in recent years to improve early identification and effective response to plant pests and diseases. In this article, the principles of convolutional neural network (CNN), recurrent neural network (RNN) and adversarial generative neural network (GAN) are explained, as well as the latest progress in the improvement and application of the three algorithms in plant disease detection and detection. By applying DL technology, efficient and accurate detection of pests and diseases can be achieved, thereby providing reliable support for agricultural production. It is hoped that this work will become a valuable resource for researchers studying plant pest and disease detection and provide guidance to further advance research and practice in this field. At the same time, some current challenges and issues that need to be solved are also discussed, such as data quality, target occlusion, model generalization ability, and adaptability in practical applications. Deep learning holds great promise in revolutionizing precision agriculture by providing efficient and accurate solutions for pest and disease management. |
| Keywords: | Deep Learning; Crop Diseases and Pests; Research Progress |
| DOI: | 10.57237/j.cst.2024.02.002 |
View:
Downloads:
Received: 26 February 2024
Accepted: 19 April 2024
Published Online: 24 April 2024
References
| [1] | Yang D, Li S, Peng Z, et al. MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion [J]. IEICE TRANSACTIONS on Information and Systems, 2019, 102(8): 1526-1536. |
| [2] | Sundararajan S K, Sankaragomathi B, Priya D S. Deep belief CNN feature representation based content based image retrieval for medical images [J]. Journal of medical systems, 2019, 43: 1-9. |
| [3] | Melnyk P, You Z, Li K. A high-performance CNN method for offline handwritten Chinese character recognition and visualization [J]. soft computing, 2020, 24(11): 7977-7987. |
| [4] | Li J, Mi Y, Li G, et al. CNN-based facial expression recognition from annotated rgb-d images for human–robot interaction [J]. International Journal of Humanoid Robotics, 2019, 16(04): 1941002. |
| [5] | Kumar S, Singh S K. Occluded thermal face recognition using bag of CNN ( Bo CNN) [J]. IEEE Signal Processing Letters, 2020, 27: 975-979. |
| [6] | McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity [J]. The bulletin of mathematical biophysics, 1943, 5: 115-133. |
| [7] | Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [J]. Advances in neural information processing systems, 2012, 25. |
| [8] | Liu J, Wang X. Plant diseases and pests detection based on deep learning: a review [J]. Plant Methods, 2021, 17: 1-18. |
| [9] | Kumar S, Kaur R. Plant disease detection using image processing-a review [J]. International Journal of Computer Applications, 2015, 124(16). |
| [10] | Martineau M, Conte D, Raveaux R, et al. A survey on image-based insect classification [J]. Pattern Recognition, 2017, 65: 273-284. |
| [11] | Barbedo J G A, Koenigkan L V, Halfeld-Vieira B A, et al. Annotated plant pathology databases for image-based detection and recognition of diseases [J]. IEEE Latin America Transactions, 2018, 16(6): 1749-1757. |
| [12] | Kaur S, Pandey S, Goel S. Plants disease identification and classification through leaf images: A survey [J]. Archives of Computational Methods in Engineering, 2019, 26: 507-530. |
| [13] | Sinha A, Shekhawat R S. Review of image processing approaches for detecting plant diseases [J]. IET Image Processing, 2020, 14(8): 1427-1439. |
| [14] | Sakib S, Ahmed N, Kabir A J, et al. An overview of convolutional neural network: Its architecture and applications [J]. 2019. |
| [15] | Medsker L R, Jain L C. Recurrent neural networks [J]. Design and Applications, 2001, 5(64-67): 2. |
| [16] | Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks [J]. Communications of the ACM, 2020, 63(11): 139-144. |
| [17] | Chauvin Y, Rumelhart D E. Backpropagation: theory, architectures, and applications [M]. Psychology press, 2013. |
| [18] | 樊湘鹏, 许燕, 周建平, 等. 基于迁移学习和改进 CNN 的葡萄叶部病害检测系统. [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(6). |
| [19] | 王国伟, 刘嘉欣. 基于卷积神经网络的玉米病害识别方法研究 [J]. 中国农机化学报, 2021, 42(2): 139. |
| [20] | 韦锦, 李正强, 许恩永, et al. 基于 DA2-YOLOv4 算法绿篱识别研究 [J]. 中国农机化学报, 2022, 43(9): 122. |
| [21] | 孙海燕, 陈云博, 封丁惟, 等. 基于注意力模型和轻量化 YOLOv4 的林业害虫检测方法 [J]. 计算机应用, 2022, 42(11): 3580. |
| [22] | 赵越, 赵辉, 姜永成, 等. 基于深度学习的马铃薯叶片病害检测方法 [J]. 中国农机化学报, 2022, 43(10): 183. |
| [23] | Sladojevic S, Arsenovic M, Anderla A, et al. Deep neural networks based recognition of plant diseases by leaf image classification [J]. Computational intelligence and neuroscience, 2016, 2016. |
| [24] | 王云露, 吴杰芳, 兰鹏, 等. 基于改进 Faster R-CNN的苹果叶部病害识别方法 [J]. 林业工程学报, 2022, 7(1): 153-159. |
| [25] | Li W, Wang D, Li M, et al. Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse [J]. Computers and Electronics in Agriculture, 2021, 183: 106048. |
| [26] | Syed-Ab-Rahman S F, Hesamian M H, Prasad M. Citrus disease detection and classification using end-to-end anchor-based deep learning model [J]. Applied Intelligence, 2022, 52(1): 927-938. |
| [27] | Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks [J]. Advances in neural information processing systems, 2015, 28. |
| [28] | Bianchi F M, Maiorino E, Kampffmeyer M C, et al. Recurrent neural network architectures [J]. Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis, 2017: 23-29. |
| [29] | Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural computation, 1997, 9(8): 1735-1780. |
| [30] | Pascanu R, Mikolov T, Bengio Y. Understanding the exploding gradient problem [J]. CoRR, abs/1211.5063, 2012, 2(417): 1. |
| [31] | Squartini S, Paolinelli S, Piazza F. Comparing different recurrent neural architectures on a specific task from vanishing gradient effect perspective [C]//2006 IEEE International Conference on Networking, Sensing and Control. IEEE, 2006: 380-385. |
| [32] | Graves A, Mohamed A rahman, Hinton G. Speech recognition with deep recurrent neural networks [C]//2013 IEEE international conference on acoustics, speech and signal processing. Ieee, 2013: 6645-6649. |
| [33] | Dauphin Y N, Bengio Y. Big neural networks waste capacity [J]. arXiv preprint arXiv: 1301.3583, 2013. |
| [34] | Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks [J]. Advances in neural information processing systems, 2014, 27. |
| [35] | Van Houdt G, Mosquera C, Nápoles G. A review on the long short-term memory model [J]. Artificial Intelligence Review, 2020, 53: 5929-5955. |
| [36] | Kim Y, Roh J H, Kim H Y. Early forecasting of rice blast disease using long short-term memory recurrent neural networks [J]. Sustainability, 2017, 10(1): 34. |
| [37] | Turkoglu M, Hanbay D, Sengur A. Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests [J]. Journal of Ambient Intelligence and Humanized Computing, 2019: 1-11. |
| [38] | Verma T, Dubey S. Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study [J]. Multimedia Tools and Applications, 2021, 80(19): 29267-29298. |
| [39] | Daniya T, Vigneshwari S. Deep neural network for disease detection in rice plant using the texture and deep features [J]. The Computer Journal, 2022, 65(7): 1812-1825. |
| [40] | Davida H E, Ramalakshmib K, Venkatesanc R, et al. Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model [J]. 2021. |
| [41] | Sreedevi A, Manike C. A smart solution for tomato leaf disease classification by modified recurrent neural network with severity computation [J]. Cybernetics and Systems, 2022: 1-41. |
| [42] | Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: An overview [J]. IEEE signal processing magazine, 2018, 35(1): 53-65. |
| [43] | Myerson R B. Refinements of the Nash equilibrium concept [J]. International journal of game theory, 1978, 7: 73-80. |
| [44] | Hagen N, Dereniak E L. Gaussian profile estimation in two dimensions [J]. Applied optics, 2008, 47(36): 6842-6851. |
| [45] | Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks [J]. arXiv preprint arXiv: 1511.06434, 2015. |
| [46] | Zeng M, Gao H, Wan L. Few-shot grape leaf diseases classification based on generative adversarial network [C] // Journal of Physics: Conference Series. IOP Publishing, 2021: 012093. |
| [47] | Wu Q, Chen Y, Meng J. DCGAN-based data augmentation for tomato leaf disease identification [J]. IEEE Access, 2020, 8: 98716-98728. |
| [48] | Zhao Y, Chen Z, Gao X, et al. Plant Disease Detection using Generated Leaves Based on DoubleGAN [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021: 1-1. |
| [49] | Li L, Zhang S, Wang B. Plant disease detection and classification by deep learning—a review [J]. IEEE Access, 2021, 9: 56683-56698. |
| [50] | Hu K, Liu Y, Nie J, et al. Rice pest identification based on multi-scale double-branch GAN-ResNet [J]. Frontiers in Plant Science, 2023, 14: 1167121. |
| [51] | Deshpande R, Patidar H. Tomato plant leaf disease detection using generative adversarial network and deep convolutional neural network [J]. The Imaging Science Journal, 2022, 70(1): 1-9. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us