Abstract
Plant species are often affected by conquering biotic strains and for sustainable yield more emphasis can be on the novel mitigation measures rather than traditional methods. Plant diseases are witnessed by visible effect on the leaf like the detectable change in color, texture or shape. Categorizing leaf diseases poses challenges like intensity of the disease in the leaf, resolution of the image, shot category and complex background. Literature reports myriads of architecture employing Convolutional Neural Networks for generating models that assist in detecting plant disease. This research work has merged responses from customized filters (Law’s Mask) that well define the texture pattern and learnable filters to ensure adaptive learning. Depending upon the stages of diseases in leaves, the defects occur at varying scales and at varying locations of leaves. Thus, rather than single deep stream of network, a specialized parallel multiscale stream with learnable filters that extract inherent attributes are utilized for improved performance. Experimental evaluation of the proposed methodology with end to end training on Plant Village dataset with 39 classes gives 99.17% for plant species classification and 98.61% for disease classification. For data Repository of Leaf Images with 12 species, 97.16% for plant species classification and 90.02% for leaf disease classification. MepcoTropicLeaf an Indian Ayurvedic Leaf dataset with 50 species is experimented using the proposed algorithm and reported with 90.86% of classification accuracy.
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References
Arivazhagan S, Shebiah RN, Ananthi S, Vishnu Varthini S (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 1:211–217
Ahmad N, Asif HMS, Saleem G et al (2021) Leaf image-based plant disease identification using color and texture features. Wirel Pers Commun. https://doi.org/10.1007/s11277-021-09054-2
Tan L, Lu J, Jiang H (2021) Tomato leaf diseases classification based on leaf images: a comparison between classical machine learning and deep learning methods. AgriEngineering. https://doi.org/10.3390/agriengineering3030035
Omeer AA, Deshmukh RR (2021) Improving the classification of invasive plant species by using continuous wavelet analysis and feature reduction techniques. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2020.101181
Saleem MH, Potgieter J, Arif KM (2020) Plant disease classification: a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants 9:1–17. https://doi.org/10.3390/plants9101319
Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric. https://doi.org/10.1016/j.compag.2016.07.003
Ma J, Du K, Zheng F et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric. https://doi.org/10.1016/j.compag.2018.08.048
Qiu R, Yang C, Moghimi A et al (2019) Detection of Fusarium Head Blight in wheat using a deep neural network and color imaging. Remote Sens. https://doi.org/10.3390/rs11222658
Ahmad I, Hamid M, Yousaf S et al (2020) Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity. https://doi.org/10.1155/2020/8812019
Jiang F, Lu Y, Chen Y et al (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric. https://doi.org/10.1016/j.compag.2020.105824
Liang W, Zhang H, Zhang G, Cao H (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep. https://doi.org/10.1038/s41598-019-38966-0
Chen J, Chen J, Zhang D et al (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric. https://doi.org/10.1016/j.compag.2020.105393
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci. https://doi.org/10.3389/fpls.2016.01419
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. https://doi.org/10.1016/j.compag.2018.03.032
Geetharamani G, AP J (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323–338. https://doi.org/10.1016/j.compeleceng.2019.04.011
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. https://doi.org/10.1016/j.compag.2018.01.009
Arsenovic M, Karanovic M, Sladojevic S et al (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry (Basel). https://doi.org/10.3390/sym11070939
Nanehkaran YA, Zhang D, Chen J et al (2020) Recognition of plant leaf diseases based on computer vision. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02505-x
Chen J, Zhang D, Nanehkaran YA (2020) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09669-w
Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th international conference on machine learning, ICML 2019
Saeed F, Khan MA, Sharif M et al (2021) Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2021.107164
Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2020.101182
Chen J, Yin H, Zhang D (2020) A self-adaptive classification method for plant disease detection using GMDH-Logistic model. Sustain Comput Informatics Syst. https://doi.org/10.1016/j.suscom.2020.100415
Argüeso D, Picon A, Irusta U et al (2020) Few-Shot Learning approach for plant disease classification using images taken in the field. Comput Electron Agric. https://doi.org/10.1016/j.compag.2020.105542
Dash S, Jena UR (2017) Multi-resolution Laws’ Masks based texture classification. J Appl Res Technol 15:571–582. https://doi.org/10.1016/j.jart.2017.07.005
Arun Pandian J, Geetharamani G (2019) Data for: identification of plant leaf diseases using a 9-layer deep convolutional neural network. Mendeley Data. https://doi.org/10.17632/tywbtsjrjv.1
Chouhan SS, Kaul A, Singh UP (2019) A database of leaf images: practice towards plant conservation with plant pathology. Mendeley Data. https://doi.org/10.17632/hb74ynkjcn.1
Ahila Priyadharshini R, Arivazhagan S, Arun M (2021) Ayurvedic medicinal plants identification: a comparative study on feature extraction methods. In: communications in computer and information science
Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci. https://doi.org/10.1155/2017/2917536
Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31:299–315. https://doi.org/10.1080/08839514.2017.1315516
Bhatt P, Sarangi S, Pappula S (2017) Comparison of CNN models for application in crop health assessment with participatory sensing. GHTC 2017 - IEEE glob humanit technol conf proc 2017-Janua:1–7. https://doi.org/10.1109/GHTC.2017.8239295
Zhang K, Wu Q, Liu A, Meng X (2018) Can deep learning identify tomato leaf disease? Adv Multimed. https://doi.org/10.1155/2018/6710865
Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2018.07.070
Chowdhury MEH, Rahman T, Khandakar A et al (2021) Tomato leaf diseases detection using deep learning technique. Technol Agric. https://doi.org/10.5772/intechopen.97319
Tm P, Pranathi A, Saiashritha K, et al (2018) Tomato leaf disease detection using convolutional neural networks. In: 2018 11th international conference on contemporary computing, IC3 2018
Agarwal M, Singh A, Arjaria S et al (2020) ToLeD: tomato leaf disease detection using convolution neural network. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2020.03.225
Durmus H, Gunes EO, Kirci M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th international conference on agro-geoinformatics, agro-geoinformatics 2017
Ahmed AA, Reddy GH (2021) A mobile-based system for detecting plant leaf diseases using deep learning. AgriEngineering. https://doi.org/10.3390/agriengineering3030032
Hassan SM, Maji AK, Jasiński M et al (2021) Identification of plant-leaf diseases using cnn and transfer-learning approach. Electron. https://doi.org/10.3390/electronics10121388
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Russel, N.S., Selvaraj, A. Leaf species and disease classification using multiscale parallel deep CNN architecture. Neural Comput & Applic 34, 19217–19237 (2022). https://doi.org/10.1007/s00521-022-07521-w
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DOI: https://doi.org/10.1007/s00521-022-07521-w