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ABSTRACT
The most crucial element in accurately monitoring and assessing cotton development is having effective cotton maps. In order to make decisions about governance, precision agriculture, and field management, the county-scale cotton remote sensing categorisation models must be evaluated. The main objective of this research is to propose novel hyperspectral image segmentation approach for cotton crops to monitor the crops and identify early signs of disease. The proposal for a hyperspectral image-based classification of cotton crops is made in this research. Using ‘Modified Hierarchical density-based spatial clustering of applications with noise (HDBSCAN),’ the procedure begins with the input image being segmented. Following this, features based on vegetation indices, hybrid vegetation indices, and statistical characteristics will be retrieved and trained with the classification model to ensure proper classification. Specifically, EVI, NDVI, and RVI are features that are based on vegetation indices. Using techniques like SVM, CNN, DBN, DT, and Improved Bidirectional Long Short-Term Memory (IBi-LSTM), this study replicates a stacked ensemble framework for classification. While the MHDBSCAN achieved the maximum accuracy value of 97.97%, the conventional techniques achieved limited accuracy. Thus, the MHDBSCAN far more effective at classifying the crop utilising hyperspectral image segmentation and the classification become more precise and accurate.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| Acronyms | = | Descriptions |
| DBN | = | Deep Belief Network |
| EVI | = | Enhanced Vegetation Index |
| TWshapeDTW | = | Time-Weighted Shape DTW |
| RF | = | Random Forest |
| RVI | = | Ratio Vegetation Index |
| CNN | = | Convolutional Neural Network |
| HEOM | = | Heterogeneous Euclidean – Overlap Metric |
| DT | = | Decision Tree |
| ppfSVM | = | pairwise proximity function Support Vector Machine |
| CNNCRF | = | Convolutional Neural Network With A Conditional Random Field Classifier |
| DL | = | Deep Learning |
| SVM | = | Support Vector Machine |
| DOCC | = | Deep One-Class Crop |
| CRF | = | Conditional Random Field |
| KNN | = | K-Nearest Neighbor |
| DBSCAN | = | Density-Based Spatial Clustering Of Applications With Noise |