https://doi.org/10.4081/jae.2025.1772
IBAC-Net: integrative brightness adaptive plant leaf disease classification
Published: 11 March 2025
Abstract Views: 5
PDF: 8
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
As agricultural technology continues to advance, effective classification of agricultural diseases are crucial for improving crop yield and quality. This study aims to explore an innovative approach to agricultural disease image classification based on a novel image classification model architecture. First, we design a novel model architecture for image classification that better integrates shallow and deep features. Secondly, to address potential brightness differences in images collected under varying weather conditions, we have introduced an image brightness adaptive block. This block automatically adjusts the brightness of images during the data collection and processing stages, thereby reducing image disparities caused by weather variations. This step is crucial for improving the robustness of the model and ensuring accurate identification of agricultural diseases under different environmental conditions. Additionally, drawing inspiration from the Inception architecture and employing a flexible downsampling strategy, we have designed a custom inception block to integrate shallow and deep features effectively. To validate the effectiveness of our proposed approach, we conducted experiments using an agricultural disease image dataset processed with weather effects. The experimental results demonstrate that our model exhibits higher accuracy and robustness in agricultural disease image classification tasks compared to traditional methods. The code has been uploaded to GitHub at the following address: https://github.com/bettyaya/IBAC-Net.
Abdu, A.M., Mokji, M.M., Sheikh, U.U. (2020). Deep learning for plant disease identification from disease region images. In: Intelligent Robotics and Applications: 13th Int. Conf. vol 12595. Cham, Springer.
DOI: https://doi.org/10.1007/978-3-030-66645-3_6
Arman, S.E., Bhuiyan, M.A.B., Abdullah, H.M., Islam, S., Chowdhury, T.T., Hossain, M. A. (2023). BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning. Data Brief 50:109608.
DOI: https://doi.org/10.1016/j.dib.2023.109608
Arshad, F., Mateen, M., Hayat, S., Wardah, M., Al-Huda, Z., Gu, Y.H., Al-antari, M.A. (2023). PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction. Alexandria Eng. J. 78:406-418.
DOI: https://doi.org/10.1016/j.aej.2023.07.076
Ashwini, C., Sellam, V. (2023). EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction. Neural Comput. Appl. 35:11125-11139.
DOI: https://doi.org/10.1007/s00521-023-08289-3
Bhuiyan, M.A.B., Abdullah, H.M., Arman, S.E., Rahman, S.S., Al Mahmud, K. (2023). BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases. Smart Agric. Technol. 4:100214.
DOI: https://doi.org/10.1016/j.atech.2023.100214
Cai, H., Li, J., Hu, M., Gan, C., Han, S. (2022). Efficientvit: Multi-scale linear attention for high-resolution dense prediction. arxiv:2205.14756.
Cho, S.B., Jeong, S.H., Yu, J. W., Choi, J.B., Kim, M.K. (2023). Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN. J. Intell. Fuzzy Syst. 45:8859-870.
DOI: https://doi.org/10.3233/JIFS-230561
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu. pp. 1800-1807.
DOI: https://doi.org/10.1109/CVPR.2017.195
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929.
Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Seattle. pp. 1780-1789.
DOI: https://doi.org/10.1109/CVPR42600.2020.00185
Gao, J., Xu, L., Huang, F. (2016). A spectral–textural kernel-based classification method of remotely sensed images. Neural Comp. Appl. 27:431-446.
DOI: https://doi.org/10.1007/s00521-015-1862-7
Han, D., Yun, S., Heo, B., Yoo, Y. (2021). Rethinking channel dimensions for efficient model design. Proc. EEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Nashville. pp. 732-741.
DOI: https://doi.org/10.1109/CVPR46437.2021.00079
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas. pp. 770-778.
DOI: https://doi.org/10.1109/CVPR.2016.90
Hughes D, Salathé M (2915). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060.
Kaggle.com [Internet]. 2020. Cacao diseases. Available from: https://www.kaggle.com/datasets/zaldyjr/cacao-diseases
Kaggle.com [Internet]. 2021. Coffee plant diseases. Available from: https://www.kaggle.com/datasets/coffeedisease/coffee-plant-disease
Kaggle.com [Internet]. 2021. Guava disease dataset (4 types). Available from: https://www.kaggle.com/datasets/omkarmanohardalvi/guava-disease-dataset-4-types
Kaggle.com [Internet]. 2023. Cotton plant diseases. Available from: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease
Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S., de Albuquerque, V.H.C. 2019. Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuit Syst. Signal Process 39:818–836.
DOI: https://doi.org/10.1007/s00034-019-01041-0
Krizhevsky, A., Sutskever, I., Hinton, G.E. (2017). Imagenet classification with deep convolutional neural networks. Commun. ACM 60:84-89.
DOI: https://doi.org/10.1145/3065386
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y. (2023). Efficientvit: Memory efficient vision transformer with cascaded group attention. Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Vancouver. pp. 14420-14430.
DOI: https://doi.org/10.1109/CVPR52729.2023.01386
Liu, Y., Wang, Z., Wang, R., Chen, J., Gao, H. (2023). Flooding-based MobileNet to identify cucumber diseases from leaf images in natural scenes. Comput. Electron. Agr. 213:108166.
DOI: https://doi.org/10.1016/j.compag.2023.108166
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., et al., (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Proc. IEEE/CVF Int. Conf. on Computer Vision, Montreal. pp. 10012-10022.
DOI: https://doi.org/10.1109/ICCV48922.2021.00986
Mwebaze, E., Mostipak, J., Joyce, Elliott, J., Dane, S. (2020). Cassava leaf disease classification. Kaggle. Available from: https://kaggle.com/competitions/cassava-leaf-disease-classification
Narayanan, K.L., Krishnan, R.S., Robinson, Y.H., Julie, E.G., Vimal, S., Saravanan, V., Kaliappan, M. (2022). Banana plant disease classification using hybrid convolutional neural network. Comput. Intell. Neurosci. 2022:9153699.
DOI: https://doi.org/10.1155/2022/9153699
Peng, C., Liu, Y., Yuan, X., Chen, Q. (2022). Research of image recognition method based on enhanced inception-ResNet-V2. Multimed. Tools Appl. 81:34345-34365.
DOI: https://doi.org/10.1007/s11042-022-12387-0
Raja, M.R., Jayaraj, V., Shajin, F. H., Devi, E.R. (2023). Radial basis function Neural Network optimized with Salp Swarm algorithm espoused paddy leaf disease classification. Biomed. Signal Proces. 86:105038.
DOI: https://doi.org/10.1016/j.bspc.2023.105038
Raja, P., Olenskyj, A., Kamangir, H., Earles, M. (2021). Simultaneously predicting multiple plant traits from multiple sensors via deformable CNN regression. arXiv: 2112.03205.
Sariturk, B., Seker, D.Z. (2022). A residual-inception U-Net (RIU-Net) approach and comparisons with U-shaped CNN and transformer models for building segmentation from high-resolution satellite images. Sensors (Basel) 22:7624.
DOI: https://doi.org/10.3390/s22197624
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.
DOI: https://doi.org/10.1016/j.compag.2020.105527
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N. (2020). PlantDoc: A dataset for visual plant disease detection. Proc. 7th ACM IKDD CoDS and 25th COMAD.(pp. 249-253.
DOI: https://doi.org/10.1145/3371158.3371196
Wang, A., Chen, H., Lin, Z., Han, J., Ding, G. (2024). Repvit: Revisiting mobile cnn from vit perspective. Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition. pp. 15909-15920.
DOI: https://doi.org/10.1109/CVPR52733.2024.01506
Wang, X., Zhong, M., Cheng, H., Han, J., Zhou, Y., Ren, J., Liu, M. (2022). SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module. CAAI T. Intell. Technol. 7:492-502.
DOI: https://doi.org/10.1049/cit2.12082
Yang, X., Huo, H., Wang, R., Li, C., Liu, X., Li, J. (2023). DGLT-Fusion: A decoupled global–local infrared and visible image fusion transformer. Infrared Phys. Technol. 128:104522.
DOI: https://doi.org/10.1016/j.infrared.2022.104522
Yao, J., Tran, S. N., Garg, S., & Sawyer, S. (2024). Deep learning for plant identification and disease classification from leaf images: multi-prediction approaches. ACM Comput. Surv. 56:153.
DOI: https://doi.org/10.1145/3639816
Zeiler, M.D., Fergus, R. Visualizing and understanding convolutional networks In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds.), Computer Vision – ECCV 2014. Lecture Notes in Computer Science, vol 8689. Cham, Springer. pp. 818-833.
DOI: https://doi.org/10.1007/978-3-319-10590-1_53
Zhao, F., Li, SJ., Zhang, J.J., Liu, H.Q. (2023). Convolution transformer fusion splicing network for hyperspectral image classification. IEEE Geosci. Remote Sensing Lett. 20:5501005.
DOI: https://doi.org/10.1109/LGRS.2022.3231874
Zhang, D., Huang, Y., Wu, C., Ma, M. (2023). Detecting tomato disease types and degrees using multi-branch and destruction learning. Comput. Electron. Agr. 213:108244.
DOI: https://doi.org/10.1016/j.compag.2023.108244
Zhang, L., Xie, L., Wang, Z., Huang, C. (2022). Cascade parallel random forest algorithm for predicting rice diseases in big data analysis. Electronics (Basel) 11; 1079.
DOI: https://doi.org/10.3390/electronics11071079
Zhang, Y., Cao, G., Li, X., Wang, B. (2018). Cascaded random forest for hyperspectral image classification. IEEE J. Sel. Top. Appl. 11:1082-1094.
DOI: https://doi.org/10.1109/JSTARS.2018.2809781
How to Cite
Xu, X. (2025) “IBAC-Net: integrative brightness adaptive plant leaf disease classification”, Journal of Agricultural Engineering. doi: 10.4081/jae.2025.1772.
Copyright (c) 2025 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
- Igor Kovacev, Daniele De Wrachien, Report on the 43rd International Symposium: Actual Tasks on Agricultural Engineering, 24th-27th February 2015, Opatija, Croatia , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Francisco Ayuga, Present and future of the numerical methods in buildings and infrastructures areas of biosystems engineering , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Alessio Tugnolo, Roberto Beghi, Valentina Giovenzana, Alessia Pampuri, Simone Virginio Marai, Andrea Casson, Enrico Ferrari, Riccardo Guidetti, Testing of ultrasonic vibration to speed up the remuage operation in sparkling wine production , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Giovanna Calia, Vittorio Serra, Antonio Ledda, Andrea De Montis, Green infrastructure planning based on ecosystem services multicriteria evaluation: the case of the metropolitan wine landscapes of Bordeaux , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Costanza Di Stefano, Vito Ferro, Establishing soil loss tolerance: an overview , Journal of Agricultural Engineering: Vol. 47 No. 3 (2016)
- Lorenzo Leso, Maurizio Uberti, Wasseem Morshed, Matteo Barbari, A survey of Italian compost dairy barns , Journal of Agricultural Engineering: Vol. 44 No. 3 (2013)
- Alessandro Trevisani, Eleonora Iaccheri, Angelo Fabbri, Adriano Guarnieri, Pallet standards in agri-food sector: a brief survey , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Carlos Alejandro Perez Garcia, Marco Bovo, Daniele Torreggiani, Patrizia Tassinari, Stefano Benni, 3D numerical modelling of temperature and humidity index distribution in livestock structures: a cattle-barn case study , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Wei Wang, Yuanjuan Gong, Xuewei Bai, Rui Tan, Real-time straw moisture content detection system for mobile straw granulator , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Zhongkuan Wang, Sheng Wen, Yubin Lan, Yue Liu, Yingying Dong, Variable-rate spray system for unmanned aerial applications using lag compensation algorithm and pulse width modulation spray technology , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
<< < 12 13 14 15 16 17 18 19 20 21 > >>
You may also start an advanced similarity search for this article.
