Intuitive Handcrafted Features and Multi-Layer Visual Feature Fusion for Automated Classification of Groundnut Leaf Diseases

This study offers a software determination approach to classify and categorize Groundnut leaf diseases automatically. Determining Groundnut leaf disease detection and classification using photo classification has recently garnered much attention. Accurate classification results can be obtained when effective feature extraction and learning methods are used. The handcrafted features used in this work are mainly created intuitively, in contrast to the deep feature's complicated interpretation requirements and significant training sample needs. A new feature fusion approach called multi-layer visual feature fusion (MLVSF) was created to better capture the diseases seen in Groundnut photos. The MLVSF model can improve the discriminatory strength of features used in Groundnut leaf image identification by combining deep and handcrafted elements from local binary pattern variants, bag-of-visual words, and convolutional neural networks(CNN). With its ability to improve CNN and achieve more precise classification, MLVSF outperforms other modern methods when evaluated on Groundnut image datasets and generates 98.9% classification accuracy.