Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations In the context of plant disease identification problems, it has been discovered that texture feature usage yields more favorable outcomes (Kaur et al, 2019). By using the grey-level co-occurrence matrix (GLCM) method, one may determine the area’s energy, entropy, contrast, homogeneity, moment of inertia, and other textural features (Mokhtar et al., 2015; Islam et al., 2017). Texture characteristics may be separated using FT and wavelet packet decomposition (Kaur et al, 2019). Additional features such as the Speed-up robust feature, the Histogram of Oriented Gradients, and the Pyramid Histogram of Visual Words (PHOW) have shown greater effectiveness (Kaur et al, 2019). In agriculture, the procedure of extracting features from raw data is known as