Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

[1]  Manoj Khanna,et al.  Land Suitability Analysis for Different Crops: A Multi Criteria Decision Making Approach using Remote Sensing and GIS , 2011 .

[2]  Mahmoud El-Banna,et al.  Modified Mahalanobis Taguchi System for Imbalance Data Classification , 2017, Comput. Intell. Neurosci..

[3]  Haoran Li,et al.  Learning dynamic simultaneous clustering and classification via automatic differential evolution and firework algorithm , 2020, Appl. Soft Comput..

[4]  Jonathan Oliver,et al.  Mining Malware to Detect Variants , 2014, 2014 Fifth Cybercrime and Trustworthy Computing Conference.

[5]  Wen-Shing Lee,et al.  Evaluating and ranking energy performance of office buildings using Grey relational analysis , 2011 .

[6]  S. Jozi,et al.  An integrated Shannon's Entropy–TOPSIS methodology for environmental risk assessment of Helleh protected area in Iran , 2012, Environmental Monitoring and Assessment.

[7]  Mohd Shukry Abdul Majid,et al.  Gait Classification Using Mahalanobis–Taguchi System for Health Monitoring Systems Following Anterior Cruciate Ligament Reconstruction , 2019, Applied Sciences.

[8]  Huaijun Wang,et al.  A Road Quality Detection Method Based on the Mahalanobis-Taguchi System , 2018, IEEE Access.

[9]  Anirban Mitra,et al.  On Rough Equalities and Rough Equivalences of Sets , 2008, RSCTC.

[10]  Fazhi He,et al.  A dividing-based many-objective evolutionary algorithm for large-scale feature selection , 2019, Soft Computing.

[11]  Fazhi He,et al.  Service-Oriented Feature-Based Data Exchange for Cloud-Based Design and Manufacturing , 2018, IEEE Transactions on Services Computing.

[12]  Neeraj Kumar,et al.  Smart Secure Sensing for IoT-Based Agriculture: Blockchain Perspective , 2021, IEEE Sensors Journal.

[13]  Numan Çelebi,et al.  Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem , 2011, Expert Syst. Appl..

[14]  Jing Chen,et al.  A method of multi-class faults classification based-on Mahalanobis-Taguchi system using vibration signals , 2011, The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety.

[15]  Farrukh Aslam Khan,et al.  Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems , 2017, Neurocomputing.

[16]  Bo Du,et al.  Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Sang-Hoon Byeon,et al.  Development of a Screening Method for Health Hazard Ranking and Scoring of Chemicals Using the Mahalanobis–Taguchi System , 2018, International journal of environmental research and public health.

[18]  Hong Yan,et al.  Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval , 2018, Pattern Recognit..

[19]  A. Hafezalkotob,et al.  Extended MULTIMOORA method based on Shannon entropy weight for materials selection , 2016 .

[20]  Bin Jiang,et al.  Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains. , 2020, ISA transactions.

[21]  Jiahang Yuan,et al.  Regional energy security performance evaluation in China using MTGS and SPA-TOPSIS. , 2019, The Science of the total environment.

[22]  Neeraj Kumar,et al.  Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM , 2020, Future Gener. Comput. Syst..

[23]  Bin Jiang,et al.  A Descriptor System Approach for Estimation of Incipient Faults With Application to High-Speed Railway Traction Devices , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Roderic Broadhurst,et al.  Malicious Spam Emails Developments and Authorship Attribution , 2013, 2013 Fourth Cybercrime and Trustworthy Computing Workshop.

[25]  Salvatore Greco,et al.  Rough Set Analysis of Preference-Ordered Data , 2002, Rough Sets and Current Trends in Computing.

[26]  Dhekra Souissi,et al.  GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia , 2020, Geocarto International.

[27]  Mamoun Alazab,et al.  A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU , 2020, Journal of Real-Time Image Processing.

[28]  Ali Kashif Bashir,et al.  An Efficient Ensemble VTOPES Multi-Criteria Decision-Making Model for Sustainable Sugarcane Farms , 2019, Sustainability.

[29]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[30]  P. Mahalakshmi,et al.  Mahalanobis Taguchi System based criteria selection for shrimp aquaculture development , 2009 .

[31]  Ayşegül Tuş,et al.  The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem , 2019, OPSEARCH.

[32]  Yuan-Biao Zhang,et al.  Evaluation of Economics Journals Based on Reduction Algorithm of Rough Set and Grey Correlation , 2015 .

[33]  Praveen Kumar Reddy Maddikunta,et al.  Predictive model for battery life in IoT networks , 2020, IET Intelligent Transport Systems.

[34]  B. K. Tripathy,et al.  Evaluation of Classifier Models Using Stratified Tenfold Cross Validation Techniques , 2011 .

[35]  Bo Du,et al.  Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Ning Wang,et al.  Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions , 2018, Sensors.

[37]  Praveen Kumar Reddy Maddikunta,et al.  Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges , 2020, IEEE Sensors Journal.

[38]  Jurgita Antucheviciene,et al.  Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes , 2019, Archives of Civil and Mechanical Engineering.

[39]  N. Deepa,et al.  Mahalanobis Taguchi system based criteria selection tool for agriculture crops , 2016 .

[40]  ÖzcanTuncay,et al.  Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem , 2011 .

[41]  Ganesan Kaliyaperumal,et al.  Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection , 2018, Soft Computing.

[42]  Bo Du,et al.  Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image , 2019, IEEE Transactions on Cybernetics.

[43]  C.-C. Jay Kuo,et al.  An Interpretable Compression and Classification System: Theory and Applications , 2019, IEEE Access.

[44]  Harshita Patel,et al.  A review on classification of imbalanced data for wireless sensor networks , 2020, Int. J. Distributed Sens. Networks.