A Novel Method for the Precise Determination of Mango Ripeness Level: Multi-Layer Visual Feature Fusion

Recently., there has been a lot of interest in using image classification to determine if a mango is ripe or unripe. One can get more accurate categorization results using effective feature extraction and learning. Unlike the deep feature, which requires many training samples but is challenging to interpret, this research”s handcrafted features are primarily constructed intuitively. To better capture these distinctive aspects of mango images, a new feature fusion method dubbed multi-layer visual feature fusion (ML V2F) has been developed in this research. By integrating deep features provided by convolutional neural networks, bag-of-visual words, and local binary pattern variations with handmade features., ML V2F has a good chance of improving feature discriminatory strength for Mango image identification. ML V2F outperforms other state-of-the-art techniques and improves convolutional neural networks when tested on mango image datasets, allowing for more precise categorization.