Abstract
Plant disease diagnosis in smart agriculture is a crucial issue that carries substantial economic significance on a global scale. To address this challenge, intelligent and smart agricultural solutions are currently being developed to assist farmers in implementing preventive measures to increase crop production. As deep learning technology continues to evolve, many convolutional neural network (CNN) models have emerged as highly effective for detecting plant leaf diseases. These CNN-based models require heavy computation and processing cost. So, this paper develops a new lightweight deep convolutional neural network named lightweight DenseNet (LWDN) for detection of plant leaf disease for agricultural applications. Based on the DenseNet121 architecture, the presented model comprises pruned and concatenated architecture of DenseNet121. The presented study involved training and testing a proposed model (LWDN) on the PlantVillage dataset to acquire a knowledge of plant disease features. The model was trained using a combination of partial layer freezing, transfer learning, and feature fusion techniques. Out of several models experimented with, the proposed model has 99.37% classification accuracy, a model size of 13.8 MB, with 1.5 M parameters. The proposed model has 93% fewer parameters than InceptionV3 and Xception and 90% and 50% fewer parameters compared to VGG16 and MobileNetV2, respectively. Furthermore, the proposed method has superior diagnostic capabilities compared to several prior studies and larger state-of-the-art models utilizing plant leaf images. The compact size and competitive accuracy of the LWDN model render it appropriate for real-time plant diagnosis on portable and mobile devices with restricted computational resources.
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Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:1–74
Arun RA, Umamaheswari S (2023) Effective multi-crop disease detection using pruned complete concatenated deep learning model. Expert Syst Appl 213:118905
Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Eco Inform 61:101182
Basavaiah J, Arlene Anthony A (2020) Tomato leaf disease classification using multiple feature extraction techniques. Wireless Pers Commun 115(1):633–651
Bevers N, Sikora EJ, Hardy NB (2022) Soybean disease identification using original field images and transfer learning with convolutional neural networks. Comput Electron Agric 203:107449
Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299–315
Cai Y, Zhang Z, Yan Q, Zhang D, Banu MJ (2021) Densely connected convolutional extreme learning machine for hyperspectral image classification. Neurocomputing 434:21–32
ML Cheatsheet, c2017. [Online]. Available: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html. Accessed 06 Sep 2023
Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020a) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393
Chen J, Zhang D, Nanehkaran YA (2020b) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl 79:31497–31515
Chen J, Zhang D, Suzauddola M, Zeb A (2021a) Identifying crop diseases using attention embedded MobileNet-V2 model. Appl Soft Comput 113:107901
Chen J, Zhang D, Zeb A, Nanehkaran YA (2021b) Identification of rice plant diseases using lightweight attention networks. Expert Syst Appl 169:114514
Chuanlei Z, Shanwen Z, Jucheng Y, Yancui S, Jia C (2017) Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int J Agric Biol Eng 10(2):74–83
Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing, IEEE. pp 8609–8613
Das D, Santosh KC, Pal U (2020) Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med 43:915–925
Dheeraj A, Chand S (2023) Deep learning model for automated image based plant disease classification. In: Proceedings of international conference on intelligent vision and computing (ICIVC 2022), Vol. 1. Springer Nature Switzerland. Cham, pp 21–32
Dubey SR, Jalal AS (2016) Apple disease classification using color, texture and shape features from images. SIViP 10:819–826
Fan X, Luo P, Mu Y, Zhou R, Tjahjadi T, Ren Y (2022) Leaf image based plant disease identification using transfer learning and feature fusion. Comput Electron Agric 196:106892
Fang S, Wang Y, Zhou G, Chen A, Cai W, Wang Q, Hu Y, Li L (2022) Multi-channel feature fusion networks with hard coordinate attention mechanism for maize disease identification under complex backgrounds. Comput Electron Agric 203:107486
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701
Fu L, Li S, Sun Y, Mu Y, Hu T, Gong H (2022) Lightweight-CNN for apple leaf disease identification. Front Plant Sci, 1508.
Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323–338
Gokulnath BV (2021) Identifying and classifying plant disease using resilient LF-CNN. Eco Inform 63:101283
Hanh BT, Van Manh H, Nguyen NV (2022) Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification. J Plant Dis Prot 129(3):623–634
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778
Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowledge Manag Process 5(2):1
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700–4708
Isikdogan LF, Nayak BV, Chyuan-Tyng W, Moreira JP, Rao S, Michael G (2020) Semifreddonets: Partially frozen neural networks for efficient computer vision systems. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII. Springer International Publishing, Cham, pp 193–208. https://doi.org/10.1007/978-3-030-58583-9_12
Jackulin C, Murugavalli S (2022) A comprehensive review on detection of plant disease using machine learning and deep learning approaches. Measur Sens 24:100441
Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data--recommendations for the use of performance metrics. In: 2013 Humaine association conference on affective computing and intelligent interaction. IEEE. pp 245–251
Jiang F, Lu Y, Chen Y, Cai D, Li G (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179:105824
Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N (2021) VirLeafNet: automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Eco Inform 61:101197
Kamal KC, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948
Karthik R, Alfred JJ, Kennedy JJ (2023) Inception-based global context attention network for the classification of coffee leaf diseases. Eco Inform 77:102213
Kaur P, Pannu HS, Malhi AK (2019) Plant disease recognition using fractional-order Zernike moments and SVM classifier. Neural Comput Appl 31:8749–8768
Kaya Y, GÜrsoy E (2023) A novel multi-head CNN design to identify plant diseases using the fusion of RGB images. Ecol Inform 75:101998
Kılıç C, İnner B (2022) A novel method for non-invasive detection of aflatoxin contaminated dried figs with deep transfer learning approach. Eco Inform 70:101728
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kumar S, Sharma B, Sharma VK, Sharma H, Bansal JC (2018) Plant leaf disease identification using exponential spider monkey optimization. Sustain Comput Inf Syst 28:100283
Kurmi Y, Gangwar S, Agrawal D, Kumar S, Srivastava HS (2021) Leaf image analysis-based crop diseases classification. SIViP 15(3):589–597
Liu B, Zhang Y, He D, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1):11
Liu G, Peng J, El-Latif AAA (2023) SK-MobileNet: a lightweight adaptive network based on complex deep transfer learning for plant disease recognition. Arab J Sci Eng 48(2):1661–1675
Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Montalbo FJP (2021) Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion. Biomed Signal Process Control 68:102583
Montalbo FJP (2022) Diagnosing gastrointestinal diseases from endoscopy images through a multi-fused CNN with auxiliary layers, alpha dropouts, and a fusion residual block. Biomed Signal Process Control 76:103683
Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32:11419–11441
Naik BN, Malmathanraj R, Palanisamy P (2022) Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model. Eco Inform 69:101663
Nigam S, Jain R, Marwaha S, Arora A, Haque MA, Dheeraj A, Singh VK (2023) Deep transfer learning model for disease identification in wheat crop. Eco Inform 75:102068
Pandey A, Jain K (2022) A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images. Eco Inform 70:101725
Pantazi XE, Moshou D, Tamouridou AA (2019) Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Comput Electron Agric 156:96–104
Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. Intell Decis Technol 11(3):357–373
Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:1852
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
Sachar S, Kumar A (2021) Survey of feature extraction and classification techniques to identify plant through leaves. Expert Syst Appl 167:114181
Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evolut 3(3):430–439
Sharma V, Tripathi AK, Mittal H (2023) DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection. Eco Inform 75:102025
Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A (2021) A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Comput Electron Agric 183:106042
Shoaib M, Shah B, Ei-Sappagh S, Ali A, Ullah A, Alenezi F, Gechev T, Hussain T, Ali F (2023) An advanced deep learning models-based plant disease detection: a review of recent research. Front Plant Sci 14:1158933
Shrivastava VK, Pradhan MK (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103:17–26
Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:43721–43729
Sutaji D, Yıldız O (2022) LEMOXINET: Lite ensemble MobileNetV2 and Xception models to predict plant disease. Eco Inform 70:101698
Thakur PS, Khanna P, Sheorey T, Ojha A (2022) Trends in vision-based machine learning techniques for plant disease identification: a systematic review. Expert Syst Appl 208:118117
Thakur PS, Sheorey T, Ojha A (2023) VGG-ICNN: a Lightweight CNN model for crop disease identification. Multimed Tools Appl 82(1):497–520
Tieleman T, Hinton G (2012) Rmsprop: divide the gradient by a running average of its recent magnitude. Coursera: neural networks for machine learning. COURSERA Neural Networks Mach. Learn, 17
Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Eco Inform 63:101289
Tokusumi (2020) Keras-flops calculator.
Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272–279
Turkoglu M, Yanikoğlu B, Hanbay D (2022) PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection. SIViP 16(2):301–309
Udendhran R, Balamurugan M (2021) Towards secure deep learning architecture for smart farming-based applications. Complex Intell Syst 7:659–666
Uğuz S, Uysal N (2021) Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput Appl 33(9):4133–4149
Xiao Z, Shi Y, Zhu G, Xiong J, Jianhua W (2023) Leaf disease detection based on lightweight deep residual network and attention mechanism. IEEE Access 11:48248–48258. https://doi.org/10.1109/ACCESS.2023.3272985
Yang D, Wang F, Hu Y, Lan Y, Deng X (2021) Citrus huanglongbing detection based on multi-modal feature fusion learning. Front Plant Sci 12:809506
Yu M, Ma X, Guan H (2023) Recognition method of soybean leaf diseases using residual neural network based on transfer learning. Eco Inform 76:102096
Yu T, Zhu H (2020) Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689.
Zhang S, Wu X, You Z, Zhang L (2017) Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135–141
Zhang K, Guo Y, Wang X, Yuan J, Ding Q (2019) Multiple feature reweight densenet for image classification. IEEE Access 7:9872–9880
Zhang Z, Flores P, Friskop A, Liu Z, Igathinathane C, Han X, Kim HJ, Jahan N, Mathew J, Shreya S (2022) Enhancing wheat disease diagnosis in a greenhouse using image deep features and parallel feature fusion. Front Plant Sci 13:834447
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Dheeraj, A., Chand, S. LWDN: lightweight DenseNet model for plant disease diagnosis. J Plant Dis Prot (2024). https://doi.org/10.1007/s41348-024-00915-z
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DOI: https://doi.org/10.1007/s41348-024-00915-z