https://doi.org/10.4081/jae.2023.1544
Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification
Published: 30 October 2023
Abstract Views: 797
PDF: 194
APPENDIX: 24
HTML: 4
APPENDIX: 24
HTML: 4
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Deep convolutional neural network (DCNN) has recently made significant strides in the classification and recognition of rice leaf disease. The majority of classification models perform disease image recognitions using collocation patterns including pooling layers, convolutional layers, and fully connected layers, followed by repeating this structure to complete depth increase. However, the key information of the lesion area is locally limited. That is to say, in the case of only performing feature extraction according to the above-mentioned model, redundant and low-correlation image feature information with the lesion area will be received, resulting in low accuracy of the model. For improvement of the network structure and accuracy promotion, here we proposed a double-branch DCNN (DBDCNN) model with a convolutional block attention module (CBAM). The results show that the accuracy of the classic models VGG-16, ResNet-50, ResNet50+CBAM, MobileNet-V2, GoogLeNet, EfficientNet-B1 and Inception-V2 is lower than the accuracy of the model in this paper (98.73%). Collectively, the DBDCNN model here we proposed might be a better choice for classification and identification of rice leaf diseases in the future, based on its novel identification strategy for crop disease diagnosis.
Barbedo A., Garcia J. 2013. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus. 2:1-12.
Bharali P., Bhuyan C., Boruah A. 2019. Plant disease detection by leaf image classification using convolutional neural network. Comm. Com. Inf. Sc. 1025:194-205.
Chakraborty S., Newton A.C. 2011. Climate change, plant diseases and food security: an overview. Plant. Pathol. 60:2-14.
Chen J.D., Chen J.X., Zhang D.F., Sun Y.D., Nanehkaran Y.A. 2020. Using deep transfer learning for image-based plant disease identification. Comput. Electron. Agr. 173:105393.
Chen L., Zhang H.W., Xiao J., Nie L.Q., Shao J., Liu W., Chua T.S. 2017. SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning. Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017. pp. 6298-306.
Deb M., Dhal K.G., Mondal R., Gálvez J. 2021. Paddy Disease Classification Study: A Deep Convolutional Neural Network Approach. Opt. Memory Neural. 30:338-57.
Deb M., Garai A., Das A., Dhal K.G. 2022. LS-Net: a convolutional neural network for leaf segmentation of rosette plants. Neural. Comput. Appl. 34:18511-24.
Dettmers T., Minervini P., Stenetorp P., Riedel S. 2018. Convolutional 2D knowledge graph embeddings. 32nd AAAI Conf. Artif. Intell. AAAI. pp. 1811-8.
Elfwing S., Uchibe E., Doya K. 2018. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks. 107:3-11.
Ghosal S., Sarkar K. 2020. Rice Leaf Diseases Classification Using CNN with Transfer Learning. 2020 IEEE Calcutta Conf. CALCON 2020 - Proc. 230-6.
He K.M., Zhang X.Y., Ren S.Q., Sun J. 2016. Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. pp. 770-8.
Huang J., Wang X., Rozelle S. 2016. Technological innovations, downside risk, and the modernization of agriculture. J. Dev. Econ. 118:207-21.
Hu J., Shen L., Albanie S., Sun G., Wu E.H. 2020. Squeeze-and- Excitation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42:2011-23.
Ioffe S., Szegedy C. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 32nd Int. Conf. Mach. Learn. ICML. 1:448-56.
Jiang F., Lu Y., Chen Y., Cai D., Li G.F. 2020. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput. Electron. Agr. 179:105824.
Kaur P., Harnal S., Gautam V., Singh M.P., Singh S.P. 2022. A novel transfer deep learning method for detection and classification of plant leaf disease. J. Amb. Intel. Hum. Comp. 14:12407-24.
Khan M.A., Kim Y.H., Choo J. 2018. Intelligent Fault Detection via Dilated Convolutional Neural Networks. Proc. - 2018 IEEE Int. Conf. Big Data Smart Comput. Big Comp. pp. 729-31.
Liu B., Zhang Y., He D.J., Li Y.X. 2018. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry. 10.
Nandhini N., Bhavani R. 2020. Feature extraction for diseased leaf image classification using machine learning. 2020 Int. Conf. Comput. Commun. Informatics, ICCCI. pp. 22-5.
Omer S.M., Ghafoor K.Z., Askar S.K. 2022. An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm. Mob. Inf. Syst. 2022:1-16.
Panchal P., Raman V.C., Mantri S. 2019. Plant Diseases Detection and Classification using Machine Learning Models. CSITSS 2019 - 2019 4th Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. Proc. 4:1-6.
Pandian A.J., Kanchanadevi K., Kumar D.V., Jasinska E., Gono R., Leonowicz Z., Jasinski M. 2022. A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection. Electronics-Switz. 11.
Peng S.B., Tang Q.Y., Zou Y.B. 2009. Current status and challenges of rice production in China. Plant. Prod. Sci. 12:3-8.
Rahman C.R., Arko P.S., Ali M.E., Iqbal Khan M.A., Apon S.H., Nowrin F., Wasif A. 2020. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst. Eng. 194:112-20.
Ray D.K., Ramankutty N., Mueller N.D., West P.C., Foley J.A. 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3:1293.
Savary S., Willocquet L., Pethybridge S.J., Esker P., McRoberts N., Nelson A. 2019. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3:430-9.
Sethy P.K., Barpanda N.K., Rath A.K., Behera S.K. 2020. Deep feature-based rice leaf disease identification using support vector machine. Comput. Electron. Agr. 175:105527.
Shi Y., Wang X.F., Zhang S.W., Zhang C.L. 2015. PNN based crop disease recognition with leaf image features and meteorological data. Int. J. Agric. Biol. Eng. 8:60-8.
Skamnioti P., Gurr S.J. 2009. Against the grain: safeguarding rice from rice blast disease. Trends Biotechnol. 27:141-50.
Subetha T., Khilar R., Subaja Christo M. 2021. WITHDRAWN: A comparative analysis on plant pathology classification using deep learning architecture – Resnet and VGG19. Mater. Today Proc.
Sundaram R.M., Vishnupriya M.R., Biradar S.K., Thakur R.P., Rao G.J. 2014. Molecular mapping of quantitative trait loci for blast resistance in rice. Rice. 7:1-12.
Valent B., Khang C.H. 2010. Recent advances in rice blast effector research. Curr. Opin. Plant. Biol. 13:434-41.
Waheed A., Goyal M., Gupta D., Khanna A., Hassanien A.E., Pandey H.M. 2020. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput. Electron. Agr. 175:105456.
Woo S., Park J., Lee J.Y., Kweon I.S. 2018. CBAM: Convolutional block attention module. ECCV. pp. 3-19.
Yu Y.Y., Liu M.Z., Feng H.J., Xu Z.H., Li Q. 2020. Split- Attention Multiframe Alignment Network for Image Restoration. IEEE Access. 8:39254-72.
Zeng W.H., Li M. 2020. Crop leaf disease recognition based on Self-Attention convolutional neural network. Comput. Electron. Agr. 172:105341.
Zeng W.H., Li H.D., Hu G.S., Liang D. 2022. Lightweight dense scale network (LDSNet) for corn leaf disease identification. Comput. Electron. Agr. 197:106943.
Zhang J., Zhang W. 2010. Support vector machine for recognition of cucumber leaf diseases. Proc. - 2nd IEEE Int. Conf. Adv. Comput. Control. ICACC. 5:264-6.
Zhang S.W., Zhang S.B., Zhang C.L., Wang X.F., Shi Y. 2019. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput. Electron. Agr. 162:422-30.
Zhao S.Y., Peng Y., Liu J.Z., Wu S. 2021. Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module. Agriculture. 11:651.
How to Cite
Bi, X. and Wang, H. (2023) “Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification”, Journal of Agricultural Engineering, 55(1). doi: 10.4081/jae.2023.1544.
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Evelia Schettini, Giovanni Puglisi, Fabiana Convertino, Fernando Antonio Cancellara, Giuliano Vox, Greenhouse localized heating powered by a polygeneration system , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Francesco M. Tangorra, Aldo Calcante, Stefano Nava, Gabriele Marchesi, Massimo Lazzari, Design and testing of a GPS/GSM collar prototype to combat cattle rustling , Journal of Agricultural Engineering: Vol. 44 No. 2 (2013)
- Elio Dinuccio, Jacopo Maffia, Carla Lazzaroni, Gianfranco Airoldi, Paolo Balsari, Davide Biagini, Clinoptilolite (E567), a natural zeolite, inclusion in heavy-pig diets: effect on the productive performance and gaseous emissions during fattening and manure storage , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Aldo Calcante, Roberto Oberti, Francesco M. Tangorra, Definition of linear regression models to calculate the technical parameters of Italian agricultural tractors , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Lucio Brunetti, Ferruccio Giametta, Pasquale Catalano, Francesco Villani, Jonathan Fioralba, Flavio Fucci, Giovanna La Fianza, Energy consumption and analysis of industrial drying plants for fresh pasta process , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Chantal Erbino, Alessandro Toccolini, Ilda Vagge, Paolo Stefano Ferrario, Guidelines for the design of a healing garden for the rehabilitation of psychiatric patients , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Daniela Lovarelli, Elisabetta Riva, Gabriele Mattachini, Marcella Guarino, Giorgio Provolo, Assessing the effect of barns structures and environmental conditions in dairy cattle farms monitored in Northern Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Bhola Paudel, Jayanta Kumar Basak, Seong Woo Jeon, Gun Ho Lee, Nibas Chandra Deb, Sijan Karki, Hyeon Tae Kim, Working speed optimization of the fully automated vegetable seedling transplanter , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Stanimir Stoilov, Salvatore F. Papandrea, Georgi Angelov, Delyan Oslekov, Giuseppe Zimbalatti, Andrea R. Proto, Productivity analysis and costs of wheel cable skidder during salvage logging in European beech stand , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Fabiana Convertino, Ileana Blanco, Evelia Schettini, Giuliano Vox, A nature-based system for improving Mediterranean buildings’ performance: contribution to energy saving by heat transfer reduction and influence of climatic parameters , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
<< < 18 19 20 21 22 23 24 25 26 27 > >>
You may also start an advanced similarity search for this article.
