## 1 Introduction
This technique is based on clustering, and the Haar filter is used to carefully extract features in the wavelet transform domain. Decomposition is performed at the first, second, and third levels in the wavelet domain, and the resulting coefficients at each level are examined to estimate the freshness of fish samples [17]. Sheep muscle classification is achieved using a wavelet analysis of near-infrared (NIR) hyperspectral imaging data. Wavelet transformation is applied to determine the optimal wavelet features for classifying sheep muscles [18]. To obtain 64-dimensional characteristics with excellent quality, eight image texture features are extracted in each of the final eight wavelengths, using a two-dimensional wavelet transformation [15]. Half-pig carcass can be accurately depicted using spectral wavelet graphics called spectral weight. Further, spectral weight is applied as a prediction model to weigh different pieces of pork and determine the tissue composition [19]. The use of the transformation of DWTLSTM is recommended, together with interference for preventing e-nose signal contamination to evaluate beef quality [20]. To estimate TVB-N values in cooked beef during storage, this study examines the integration of spectral and image data from visible and near-infrared hyperspectral imaging. Nine ideal wavelengths are selected, using the Sequential Projection Algorithm (SPA) and Variables Without Elimination (VWE). The discrete wavelet transform (DCT) extracts 36 single values as texture features [21]. This study uses image preprocessing, dataset training, and classification to assess the quality of tuna meat classes according to color space. The three main steps in image preprocessing are cropping, converting RGB images to HSV, and extracting features using wavelets. The findings show that Symlet waves produce higher correlation coefficients among feature extraction rates than Haar waves. Wavelet Symlet and k-NN classify 65 test image datasets with a higher accuracy of 81.8% than wavelet Haar and k-NN, which is 80.3% [22]. The amount of intramuscular fat (IMFAT) in beef ribeye muscle has been predicted using a multiresolution texture analysis technique, using wavelet transformation. Several features are calculated based on 2-D wavelet decomposition ultrasonic images using Haar wavelets as the basis function to create a fast wavelet transform [23]. A new technique for local thresholding using weighted detail coefficients in wavelet synthesis is adopted to generate multi-scale thresholding functions for image segmentation. The fast wavelet technique can implement such a local wavelet-based thresholding method, which is adapted to the local environment and size. Researchers used X-ray imaging for this method to detect physical contamination in chicken inspections [24]. These features are extracted carefully, using a Haar filter in the form of a domain wavelet transform. Decomposition at the first, second, and third levels in the wavelet domain was performed, and the resulting coefficients at each level were examined to estimate the freshness of the fish samples [25]. The amount and percentage of IMFAT are the main elements determining beef quality. Texture analysis is used on B-mode ultrasound images of live bovine cow muscle ribeye to predict its IMFAT. GLCM is applied for multiresolution analysis of textures and second-order statistics using wavelet transform (WT) [26]. This research aims to distinguish between beef and pork using digital photos. For texture analysis, color features use a GLCM to calculate the first-order statistical average of color values [27]. There is a technique to extract the texture of beef, which is a vital component in beef classification. Texture feature analysis extraction is carried out using the frequency domain Discrete Wavelet Transform (DWT) and GLCM statistics [28]. This research aims to examine the freshness level of chicken meat by analyzing its color and texture. GLCM is the texture property used [29].