Efficient Weed Classification and Disease MANAGEMENT using Multivariate Principle Component Analysis on Spectral Evolution Using Change Detection Methods

Weeds are unwanted plant or crops in the agriculture region which leads to primary pest problem in modern agriculture farming. In order to control area specific weed control on basis of classification and management of disease in farmland, hyperspectral images have been acquired from the satellite images in the remote sensing area. With different observation conditions and sensor characteristics, hyperspectral image classification based on spectral evolution simultaneously extracts the sets of spectral signatures of endmembers and maps the corresponding abundance maps from multiple spectral images. It then utilizes multiple supervised and unsupervised mechanisms for class-specific variations on weed and its diseases. Obviously mapping method degrades on accuracy of the coupling of the spectral evolution simultaneously. In this paper, a novel efficient weed classification and disease management on spectral evolution mapping should be proposed using Multivariate principle component analysis. It is examined as change detection mechanism which explores variation in the class features efficiently as the context of images is basis of bands of weed plant and its associated plant diseases, further it leads to a good tradeoff between wider receptive field and the use of Context is employed towards mapping Agriculture Land cover spectral evolution in the hyperspectral images. Proposed approach is capable of computing the spectral correlation among two images with respect to spectral similarity. Finally, it predicts the large intra class variation of weed accurately on temporal changes of the agriculture surfaces along various climate seasons and fields. Experimental analysis of the proposed mechanism was validated on Landsat 8 dataset to compute overall accuracy of the model on the changes in the weed and its diseases. The results of the work exhibits that proposed model can enhance the classification accuracy and reduces the differences of multi-temporal effects compared with existing state of art approaches.