An hybrid fuzzy based deep convolutional neural networks for big data based hyper-spectral image classification in agricultural applications

Hyperspectral image delves into hundreds of distinct spectral bands, unveiling an intricate 'fingerprint' of light reflected by crops, soil, and various environmental elements. This detailed view enables precise identification in the realm of agricultural practices. This information's intricate analysis and categorisation demand substantial computational prowess and storage capacity. The realm of big data frameworks and distributed computing realms has become paramount in efficiently managing this abundance. Within hyper-spectral images, pixels often encapsulate a medley of materials within a singular spatial entity, presenting a challenge in accurate pixel classification, particularly amidst intricate landscapes or overlapping spectral imprints. Furthermore, certain bands may prove superfluous or harbour irrelevant data. Tackling noise and redundancy necessitates employing refined preprocessing methods to enhance classification precision. To overcome the fallback in achieving improved accuracy, this proposed work uses a Hybrid Fuzzy Deep Convolutional Neural Networks (HFDCNN) model for classifying the big data of hyperspectral images. The proposed work is analysed using a real-time self-created dataset created using hyperspectral images of crops and soil across various parts of Tamilnadu. The proposed work is analysed in terms of accuracy, precision, recall, and F score, and it is compared with the existing state-of-the-art methods to prove its supremacy.