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Concatenated deep features with modified LSTM for enhanced crop disease classification

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Abstract

In the agriculture industry, which is the backbone of our country, attaining healthy crops is a critical task. Unidentified crop diseases can cause significant losses in the agriculture sector, thus they must be recognized and detected quickly. Crop infections can be identified and detected correctly, potentially saving harvests from spoilage. Due to the appearance differences and crowded backdrop among crop illnesses, automatic crop disease detection in the wild is a difficult issue in current intelligent agriculture. The main intention of this work is to implement a novel deep learning model for crop disease classification. In the data collection phase, the benchmark dataset is used, which is pre-processed by median filtering, and contrast enhancement techniques. Once the image is pre-processed, the abnormality segmentation from the leaves is performed by the Optimized Fuzzy C-Means Clustering (FCM). The improvement of FCM is done based on hybrid meta-heuristic algorithm with Galactic Swarm Optimization (GSO), and Rider Optimization Algorithm (ROA) called Hybrid Galactic-Rider Optimization Algorithm (HG-ROA). From the segmented images, the TGC features and deep features are concatenated. The TCG features are Texture features, Geometrical features, and Color Features, and the features from the Convolutional Neural Network (CNN) are considered as the deep features. These two sets of features are dimensionally reduced by the Principle Component Analysis (PCA). With these features, the modified Long short-term memory (LSTM) with HG-ROA-based improvement optimally classified the types of diseases from different plants. When compared to traditional approaches, the suggested methodology obtains the best classification accuracy.

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Abbreviations

\(H_q^{med}\) :

Pre-processed images

\(HH\left( {z\left( {e,f} \right)} \right)\) :

Histogram equalization

\(CG\left( {y\left( {e,f} \right)} \right)\) :

Pixel's intensity

\(J_m = \mathop {\lim }\limits_{y \to \infty } g\left( y \right)\) :

The intensity at the left side of the edge

\(J_s = \mathop {\lim }\limits_{y \to \infty } g\left( y \right)\) :

The intensity at the right side of the edge

\(FET_m^{geo}\) :

Geometric features

\(FET_o^{ext} = \left\{ {FET_k^{glcm} ,FET_l^{clr} ,FET_m^{geo} ,FET_n^{cnn} } \right\}\) :

Total retrieved features from the abnormality segmented images

\(CTUT_{kt}\) :

Eigenvectors

\(FET_p^{pca}\) :

Dimensionality reduced features from PCA

\(TE_{ie,je}^{te}\) :

Steering angle of the rider vehicle

\(XE_{LE}^{ie}\) :

Minimum value

\(TE_{OFF}\) :

Off time

\(XE_{UE}^{ie}\) :

Highest value

References

  • Abbasi, M.U., Rashad, A., Basalamah, A., Tariq, M.: Detection of epilepsy seizures in neo-natal EEG using LSTM architecture. IEEE Access 7 (2019)

  • Ambati, L.S., Narukonda, K., Bojja, G.R., Bishop, D.: Factors influencing the adoption of artificial intelligence in organizations-from an employee's perspective. Adoption of AI in Organization from Employee Perspective (2020b).

  • Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classication of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 15(1), 211–217 (2013)

    Google Scholar 

  • Beed, F.D.: Managing the biological environment to promote and sustain crop productivity and quality. Food Secur. 6(2), 169–186 (2014)

    Article  Google Scholar 

  • Binu, D., Kariyappa, B.S.: RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Trans. Instrum. Measur. 68, 2–26 (2018)

    Article  Google Scholar 

  • Bojja, G.R., Ofori, M., Liu, J., Ambati, L.S.: Early public outlook on the coronavirus disease (COVID-19): a social media study. Social Media Analysis on Coronavirus (COVID-19) (2020)

  • Chaudhary, A., Thakur, R., Kolhe, S., Kamal, R.: A particle swarm optimization based ensemble for vegetable crop disease recognition. Comput. Electron. Agric. 178, 105747 (2020)

    Article  Google Scholar 

  • Chuanlei, Z., Shanwen, Z., Jucheng, Y., Yancui, S., Jia, C.: Apple leaf disease identication using genetic algorithm and correlation based feature selection method. Int. J. Agric. Biol. Eng. 10(2), 74–83 (2017)

    Google Scholar 

  • Dai, Q., Cheng, X., Qiao, Y., Zhang, Y.: Crop leaf disease image super-resolution and identification with dual attention and topology fusion generative adversarial network. IEEE Access 8, 55724–55735 (2020)

    Article  Google Scholar 

  • Di Cicco, M., Potena, C., Grisetti, G., Pretto, A.: Automatic Model Based Dataset Generation for Fast And Accurate Crop and Weeds Detection. arXiv preprint (2016)

  • Farooqui, N.A., Mishra, A.K., Mehra, R.: Research on automatic crop disease recognition using heuristic-based concatenated deep learning model. Communications (2021)

  • Gao, H., Gao, T., Cheng, R.: Robust detection of median filtering based on data-pair histogram feature and local configuration pattern. J. Inf. Secur. Appl. 53, 102506 (2020a)

    Google Scholar 

  • Gao, J., Qing, L., Li, L., Cheng, Y., Peng, Y.: Multi-scale features based interpersonal relation recognition using higher-order graph neural network. Neurocomputing 456, 243–252 (2021)

    Article  Google Scholar 

  • Gutiérrez, P.A., López-Granados, F., Pena-Barragán, J.M., Jurado-Expósito, M., Hervás- Martínez, C.: Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Comput. Electron. Agric 64(2), 293–306 (2008)

    Article  Google Scholar 

  • Hadri, A., Chougdali, K., Touahni, R.: Intrusion detection system using PCA and fuzzy PCA techniques. IEEE Access (2016)

  • Hamuda, E., Mc Ginley, B., Glavin, M., Jones, E.: Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm. Comput. Electron. Agric. 148, 37–44 (2018)

    Article  Google Scholar 

  • Hassija, V., Batra, S., Chamola, V., Anand, T., Goyal, P., Goyal, N., Guizani, M.: A blockchain and deep neural networks-based secure framework for enhanced crop protection. Ad Hoc Netw. 119, 102537 (2021)

    Article  Google Scholar 

  • Hill, M.G., Connolly, P.G., Reutemann, P., Fletcher, D.: The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Comput. Electron. Agric 108(1), 250–257 (2014)

    Article  Google Scholar 

  • Jafari, M., Chaleshtari, M.H.B.: Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out. Eur. J. Mech. A 66, 1–14 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  • Ji, S., Zhang, C., Xu, A., Shi, Y., Duan, Y.: 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens. 10(1), 75–84 (2018)

    Article  Google Scholar 

  • Ji, M., Zhang, K., Qiufeng, Wu., Deng, Z.: Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks. Soft. Comput. 24, 15327–15340 (2020)

    Article  Google Scholar 

  • Jiang, Y., Chen, W., Liu, M., Wang, Y., Meijering, E.: 3D neuron microscopy image segmentation via the ray-shooting model and a DC-BLSTM network. IEEE Trans. Med. Imaging 40(1), 26–37 (2021)

    Article  Google Scholar 

  • Jose, A., Ravi, S., Sambath, M.: Brain tumor segmentation using K-means clustering and fuzzy C-means algorithms and its area calculation. Int. J. Innov. Res. Comput. Commun. Eng. 2(3) (2014)

  • Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S., Victorde Albuquerque, H.C.: Seasonal crops disease prediction and classification using deep convolutional encoder network. Circ. Syst. Signal Process. 39, 818–836 (2019)

    Article  Google Scholar 

  • Kolhe, S., Kamal, R., Saini, H.S., Gupta, G.K.: An intelligent multimedia interface for fuzzy-logic based inference in crops. Expert Syst. Appl 38(12), 14592–14601 (2011)

    Article  Google Scholar 

  • Le, T.-T., Lin, C.-Y., Piedad, E.: Deep learning for noninvasive classification of clustered horticultural crops—a case for banana fruit tiers. Postharvest Biol. Technol. 156, 110922 (2019)

    Article  Google Scholar 

  • Li, Y., Hanxiang Wang, L., Dang, M., Sadeghi-Niaraki, A., Moon, H.: Crop pest recognition in natural scenes using convolutional neural networks. Comput. Electron. Agric. 169, 105174 (2020)

    Article  Google Scholar 

  • Mahlein, A.K., Oerke, E.C., Steiner, U., Dehne, H.W.: Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol 133, 197–209 (2012)

    Article  Google Scholar 

  • Martinez, J.A.C., La Rosa, L.E.C., Feitosa, R.Q., Sanches, I.D., Happ, P.N.: Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences. ISPRS J. Photogram. Remote Sens. 171, 188–201 (2021)

    Article  Google Scholar 

  • Muthiah-Nakarajan, V., Noel, M.M.: Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion. Appl. Soft Comput. 38, 771–787 (2016)

    Article  Google Scholar 

  • Oerke, E.-C.: Crop losses to pests. J. Agric. Sci. 144(1), 31–43 (2006)

    Article  Google Scholar 

  • Oerke, E.C., Gerhards, R., Menz, G., Sikora, R.A.: Precision Crop Protection-The Challenge and Use of Heterogeneity, vol. 5. Springer, Dordrecht (2010)

    Book  Google Scholar 

  • Oerke, E.C., Dehne, H.W., Schönbeck, F.: Weber A “Crop Production and Crop Protection: Estimated Losses in Major Food and Cash Crops.” Elsevier, Amsterdam (2012)

    Google Scholar 

  • Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., Johannes, A.: Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput. Electron. Agric. 161, 280–290 (2019)

    Article  Google Scholar 

  • Ramesh, D., Jose, D., Keerthana, R., Krishnaveni, V.: Detection of pulmonary nodules using thresholding and fractal analysis. In: Computational Vision and Bio Inspired Computing, pp. 937–946. (2018)

  • Sai, S.M., Gopichand, G., Reddy, C.V., Teja, K.M.: High accurate unhealthy leaf detection. Comput. Vis. Pattern Recogn. (2019)

  • Savary, S., Ficke, A., Aubertot, J.-N., Hollier, C.: Crop losses due to diseases and their implications for global food production losses and food security. Food Secur. 4(4), 519–537 (2012a)

    Article  Google Scholar 

  • Savary, S., Nelson, A., Willocquet, L., Pangga, I., Aunario, J.: Modeling and mapping potential epidemics of rice diseases globally. Crop Prot. 34, 6–17 (2012b)

    Article  Google Scholar 

  • Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., Nelson, A.: The global burden of pathogens and pests on major food crops. Nature Ecol. Evol 3(3), 430 (2019)

    Article  Google Scholar 

  • Sood, S., Singh, H., Malarvel, M.: Image quality enhancement for Wheat rust diseased images using Histogram equalization technique. In: International Conference on Computing Methodologies and Communication (ICCMC), pp. 1035–1042 (2021)

  • Srivastava, D., Wadhvani, R., Gyanchandani, M.: A review: color feature extraction methods for content based image retrieval. IJCEM Int. J. Comput. Eng. Manag. 18(3), 9–13 (2015)

    Google Scholar 

  • Sun, J., Tan, W., Mao, H., Wu, X., Chen, Y., Wang, L.: Recognition of multiple plant leaf diseases based on improved convolutional neural network. Trans. Chin. Soc. Agric. Eng. 33(19), 209–215 (2017)

    Google Scholar 

  • Tabjula, J., Kalyani, S., Rajagopal, P., Srinivasan, B.: Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves. Struct. Health Monit. (2021a)

  • Tabjula, J.L., Kanakambaran, S., Kalyani, S., Rajagopal, P., Srinivasan, B.: Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Struct. Control Health Monit. 28, e2690 (2021b)

    Article  Google Scholar 

  • Tiwari, V., Joshi, R.C., Dutta, M.K.: Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol. Inform. 63, 101289 (2021b)

    Article  Google Scholar 

  • Vallabhajosyula, S., Sistla, V., Kolli, V.K.K.: Transfer learning-based deep ensemble neural network for plant leaf disease detection. J. Plant Dis. Protect. 129, 545–558 (2022)

    Article  Google Scholar 

  • Xiong, Y., Liang, L., Wang, L., She, J., Min, W.: Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput. Electron. Agric. 177, 105712 (2020)

    Article  Google Scholar 

  • Xu, W., Wang, Q., Chen, R.: Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks. GeoInformatica 22, 363–381 (2017)

    Article  Google Scholar 

  • Yang, B., Chen, W., Luo, H., Tan, Y., Liu, M., Wang, Y.: Neuron image segmentation via learning deep features and enhancing weak neuronal structures. IEEE J. Biomed. Health Inform. 25(5), 1634–1645 (2021)

    Article  Google Scholar 

  • Zhou, J., Li, J., Wang, C., Huarui, W., Zhao, C., Teng, G.: Crop disease identification and interpretation method based on multimodal deep learning. Comput. Electron. Agric. 189, 106408 (2021a)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, DIT University, Dehradun, India

    Nafees Akhter Farooqui & Amit Kumar Mishra

  2. School of Computer Science and Engineering, Dev Bhoomi Uttarakhand University, Dehradun, India

    Ritika Mehra

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  1. Nafees Akhter Farooqui
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Farooqui, N.A., Mishra, A.K. & Mehra, R. Concatenated deep features with modified LSTM for enhanced crop disease classification. Int J Intell Robot Appl 7, 510–534 (2023). https://doi.org/10.1007/s41315-022-00258-8

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