Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification

Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Margesh Keskar, Dhananjay D Maktedar
DOI : 10.14445/22315381/IJETT-V70I6P220

How to Cite?

Margesh Keskar, Dhananjay D Maktedar, "Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 165-184, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P220

Abstract
Plants can be hypothesized to be the inevitable need of living beings on earth. Amongst the gigantically large plant species and varieties, the medicinal plants have a distinct and significant role in herbal remedies, ayurvedic medicine, the pharmaceutical industry, and the major modern medicine world. Various medicinal plants like roots, stems, and leaves are used for the abovementioned purposes; however, their efficacy depends on their intrinsic health condition. In other words, a medicinal plant with healthy and non-contentious characteristics can positively impact medicinal uses. On the contrary, plants with the disease can have a negative or insignificant impact on medicinal purposes. In sync with this fact, detecting plant disease over the different medicinal plants can be vital for healthy plant selection and identifying diseases for preventive measures or decisions. Despite the robustness of the vision-based automatic plant disease detection and classification systems, the non-uniform disease patterns, non-ROI feature learning, and inferior feature space confine the efficacy of the major athand solutions. To alleviate such limitations, in this paper, a highly robust evolutionary computing-driven ROI-specific Spatio-temporal statistical feature learning model is developed for medicinal plant disease detection and classification. To ensure solution optimality, the proposed model first performed pre-processing employing image histogram equalization, intensity equalization, and Z-score normalization, followed by annotations. Subsequently, a first-of-its-kind Firefly algorithm-driven Fuzzy C-Means clustering (FFCM) was developed for ROI segmentation. Subsequently, the proposed model performed an ROI-specific color space overlay to reconstruct ROI in RGB color space to extract significant Spatio-temporal statistical or textural features. In the proposed model, eight Gray-level co-occurrence metrics named correlation, heterogeneity, entropy, energy, contrast, mean, standard deviation, and variance were extracted as STTF features, which were subsequently applied to perform two-class classification for healthy and diseased medicinal plant classification. The simulated results revealed that the proposed model yields superior medicinal plant disease detection and classification performance in terms of accuracy (98.62%), precision (98.81%), recall (98.79%), and F-Measure (0.988).

Keywords
GLCM Features Heuristic-based ROI Segmentation, Medicinal Plant Disease Detection, Neuro-computing.

Reference
[1] S. Naeem, A. Ali, C. Chesneau, M. H. Tahir, F. Jamal, R. A. K. Sherwani, and M. U. Hassan, The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach, Agronomy. 11 (2021) 263.
[2] Dahigaonkar T.D, Kalyane R, Identification of Ayurvedic Medicinal Plants by Image Processing of Leaf Samples, Int. Res. J. Eng. Technol IRJEF. 5 (2018) 351–355.
[3] Sabarinathan C, Hota A, Raj A, Dubey V.K, Ethirajulu V, Medicinal Plant Leaf Recognition and Show Medicinal Uses Using Convolutional Neural Network, Int. J. Glob. Eng. 1 (2018) 120–127.
[4] Wallelign S, Polceanu M, Buche C, Soybean Plant Disease Identification Using Convolutional Neural Network, In Proceedings of the Thirty-First International Flairs Conference, Melbourne, FL, USA. (2018).
[5] Simion I.M, Casoni D, Sârbu C, Classification of Romanian Medicinal Plant Extracts According to the Therapeutic Effects Using Thin Layer Chromatography and Robust Chemometrics, J. Pharm. Biomed. Anal. 163 (2019) 137–143.
[6] Dhingra G, Kumar V, Joshi H.D, A Novel Computer Vision Based Neutrosophic Approach for Leaf Disease Identification and Classification, Measurement. 135 (2019) 782–794.
[7] Turkoglu M, Hanbay D, Leaf-Based Plant Species Recognition Based on Improved Local Binary Pattern and Extreme Learning Machine. Phys. A Stat. Mech. Appl. 527 (2019) 121297.
[8] Qadri S, Furqan Qadri S, Husnain M, Saad Missen M.M, Khan D.M, Muzammil-Ul-Rehman Razzaq A, Ullah S, Machine Vision Approach for Classification of Citrus Leaves Using Fused Features, Int. J. Food Prop. 22 (2019) 2072–2089.
[9] P. Bose, S. Dutta, V. Goyal and S. K. Bandyopadhyay, Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes, Preprints www.preprints.org. (2021) 1-7. Doi:10.20944/preprints202107. 0638.v1.
[10] B. Bose, J. Priya, S. Welekar, and Z. Gao, HEMP Disease Detection and Classification Using Machine Learning and Deep Learning, IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking ISPA/BDCloud/SocialCom/SustainCom. (2020) 762-769.
[11] M. Kumar, A. Kumar and V. S. Palaparthy, Soil Sensors-Based Prediction System for Plant Diseases Using Exploratory Data Analysis and Machine Learning, in IEEE Sensors Journal. 21(16) (2021) 17455-17468.
[12] K. S. Patle, R. Saini, A. Kumar, S. G. Surya, V. S. Palaparthy and K. N. Salama, IoT Enabled, Leaf Wetness Sensor on the Flexible Substrates for In-Situ Plant Disease Management, in IEEE Sensors Journal. 21(17) (2021) 19481-19491.
[13] D. Ashourloo, H. Aghighi, A. A. Matkan, M. R. Mobasheri and A. M. Rad, An Investigation into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(9) (2016) 4344-4351.
[14] M. I. Hussein, D. Jithin, I. J. Rajmohan, A. Sham, E. E. M. A. Saeed and S. F. AbuQamar, Microwave Characterization of Hydrophilic and Hydrophobic Plant Pathogenic Fungi Using Open-Ended Coaxial Probe, in IEEE Access. 7 (2019) 45841-45849.
[15] N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad and S. Berman, Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus, in IEEE Robotics and Automation Letters. 1(1) (2016) 354-360.
[16] X. Nie, L. Wang, H. Ding and M. Xu, Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention, in IEEE Access. 7 (2019) 170003-170011.
[17] M. Ahmad, M. Abdullah, H. Moon and D. Han, Plant Disease Detection in Imbalanced Datasets Using Efficient Convolutional Neural Networks with Stepwise Transfer Learning, in IEEE Access. 9 (2021) 140565-140580.
[18] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks, in IEEE Access. 7 (2019) 59069-59080.
[19] S. Huang, G. Zhou, M. He, A. Chen, W. Zhang, and Y. Hu, Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM, in IEEE Access. 8 (2020) 136421-136433.
[20] Y. Yuan, Z. Xu, and G. Lu, SPEDCCNN: Spatial Pyramid-Oriented Encoder-Decoder Cascade Convolution Neural Network for Crop Disease Leaf Segmentation, in IEEE Access. 9 (2021) 14849-14866.
[21] S. C. K, J. C. D and N. Patil, Cardamom Plant Disease Detection Approach Using EfficientNetV2, in IEEE Access. 10 (2022) 789-804.
[22] U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease, in IEEE Access. 7 (2019) 43721-43729.
[23] M. A. Khan et al., An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection, in IEEE Access. 7 (2019) 46261-46277.
[24] J. Sun, Y. Yang, X. He, and X. Wu, Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning, in IEEE Access. 8 (2020) 33679-33688.
[25] S. Azimi, R. Wadhawan and T. K. Gandhi, Intelligent Monitoring of Stress-Induced by Water Deficiency in Plants Using Deep Learning, in IEEE Transactions on Instrumentation and Measurement, Art no. 5017113. 70 (2021) 1-13.
[26] R. Dwivedi, T. Dutta and Y. -C. Hu, A Leaf Disease Detection Mechanism Based on L1-Norm Minimization Extreme Learning Machine, in IEEE Geoscience and Remote Sensing Letters, Art no. 8019905. 19 (2022) 1-5.
[27] A. Khattak et al., Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model, in IEEE Access. 9 (2021) 112942-112954,.
[28] W. Huang et al., New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6) (2014) 2516-2524.
[29] S. Barburiceanu, S. Meza, B. Orza, R. Malutan, and R. Terebes, Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture, in IEEE Access. 9 (2021) 160085-160103.
[30] C. Zhou, Z. Zhang, S. Zhou, J. Xing, Q. Wu and J. Song, Grape Leaf Spot Identification Under Limited Samples by Fine GrainedGAN, in IEEE Access. 9 (2021) 100480-100489.
[31] S. V. Militante, B. D. Gerardo and N. V. Dionisio, Plant Leaf Detection and Disease Recognition using Deep Learning, IEEE Eurasia Conference on IOT, Communication and Engineering ECICE. (2019) 579-582.
[32] F. Marzougui, M. Elleuch, and M. Kherallah, A Deep CNN Approach for Plant Disease Detection, 21st International Arab Conference on Information Technology (ACIT). (2020) 1-6.
[33] P. K. V, E. G. Rao, G. Anitha, and G. K. Kumar, Plant Disease Detection using Convolutional Neural Networks, 5th International Conference on Trends in Electronics and Informatics ICOEI. (2021) 1473-1476.
[34] X. Guan, A Novel Method of Plant Leaf Disease Detection Based on Deep Learning and Convolutional Neural Network, 6th International Conference on Intelligent Computing and Signal Processing ICSP. (2021) 816-819.
[35] M. A. Rahman, M. M. Islam, G. M. Shahir Mahdee and M. W. Ul Kabir, Improved Segmentation Approach for Plant Disease Detection, 1st International Conference on Advances in Science, Engineering and Robotics Technology ICASERT. (2019) 1-5.
[36] SM. Sardogan, A. Tuncer and Y. Ozen, Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm, 3rd International Conference on Computer Science and Engineering UBMK. (2018) 382-385.
[37] R. D. Devi, S. A. Nandhini, R. Hemalatha and S. Radha, IoT Enabled Efficient Detection and Classification of Plant Diseases for Agricultural Applications, International Conference on Wireless Communications Signal Processing and Networking WiSPNET. (2019) 447-451.
[38] Kirti and N. Rajpal, Black Rot Disease Detection in Grape Plant Vitis Vinifera Using Colour Based Segmentation & Machine Learning, 2nd International Conference on Advances in Computing, Communication Control and Networking ICACCCN. (2020) 976- 979.
[39] V. V. Srinidhi, A. Sahay and K. Deeba, Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks: Apple Leaves Disease Detection using EfficientNet and DenseNet, 5th International Conference on Computing Methodologies and Communication ICCMC. (2021) 1119-1127.
[40] H. F. Pardede, E. Suryawati, R. Sustika and V. Zilvan, Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases, International Conference on Computer, Control, Informatics and its Applications IC3INA. (2018) 158-162.
[41] T. Sanida, D. Tsiktsiris, A. Sideris and M. Dasygenis, A Heterogeneous Lightweight Network for Plant Disease Classification, 10th International Conference on Modern Circuits and Systems Technologies MOCAST. (2021) 1-4.
[42] A. H. Bin Abdul Wahab, R. Zahari and T. H. Lim, Detecting Diseases in Chilli Plants Using K-Means Segmented Support Vector Machine, 3rd International Conference on Imaging, Signal Processing and Communication ICISPC. (2019) 57-61.
[43] B. Bose, J. Priya, S. Welekar and Z. Gao, Hemp Disease Detection and Classification Using Machine Learning and Deep Learning, IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking ISPA/BDCloud/SocialCom/SustainCom. (2020) 762-769.
[44] [Online]. Available: https://data.mendeley.com/datasets/nntj2v3n.