Advanced Plant Disease Segmentation in Precision Agriculture Using Optimal Dimensionality Reduction With Fuzzy C-Means Clustering and Deep Learning

Analysis of hyperspectral imagery is a critical aspect of remote sensing in precision agriculture, for which effective dimensionality reduction (DR) strategies for the inherent complexity and uncertainty of the data are highly necessary. The fusion of fuzzy logic with DR techniques offers potential promises to refine enough feature information from the classification system, which may potentially compromise critical information. However, graph-based deep learning, especially the use of graph attention networks (GATs), has advanced the field by ensuring enhanced spatial and spatial-type feature relationships, which alleviates classification errors. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep-learning algorithms, such as three-dimensional convolutional neural networks (3D-CNNs), have resorted to reducing the need for and reliance on high samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach, enhanced transformation-enabled fuzzy graph (ETFG) network, was designed, which is a combination of deep fuzzy-based DR, enhanced through the use of 3D-CNN and GAT, with the application of principal component analysis (PCA) for optimized DR. The ETFG model entailed two major processing stages, where the initial stage involved classifying the raw data cube using the 3D-CNN, and then the results processed an algorithm enriched by lightweight GAT-based modules. The ETFG model combines fuzzy C-means clustering and optimized DR using PCA, contributing the best of PCA and GAT capabilities for optimized classification. At high-performance optimal DR, the ETFG model offered optimal multispectral imaging and the analysis and classification of hyperspectral data, which is promising enough to advance the field's needs.