Revolutionizing Raisin Processing through Advanced Sorting and Visualization with a Deep Learning Based Support Vector Machine Model

By using a Deep Learning-Based Support Vector Machine (SVM) model, this work presents a novel method for the sorting and visualisation of raisins. A vital component of the agricultural sector, the processing of raisins, has historically relied on labour-intensive, human error-prone manual sorting techniques. The suggested approach makes use of deep learning to improve this process' accuracy and efficiency. The Raisins dataset in the form of two classes namely Besni and Kecimen which is extracted from the Kaggle open source environment or platform, is pre-processed using the SVM model and the model is further designed accordingly so that it may predict and accuracy of 92 %. The cultural history of raisins is extensive and rich, going all the way back to antiquity. They are utilized in many different culinary applications, such as savory meals and classic desserts. Vitamins, minerals, and dietary fibre are just a few of the essential components that are concentrated in raisins. They can be used in many dishes as a healthier alternative to processed sugars because of their natural sweetness. In the context of food processing, this work investigates the use of a deep learning-based Support Vector Machine (SVM) model for sorting and displaying raisins. The manufacturing of raisins entails complex quality control procedures that have historically relied on labour-intensive techniques. This study automates the categorization of raisins according to quality factors in an effort to improve sorting accuracy and efficiency by utilizing SVMs. Known for its resilience in classification tasks, the SVM model is used to identify intricate patterns in raisin samples, which enhances sorting accuracy. Furthermore, the research delves into the visual element, illuminating the structural features of raisins and offering valuable perspectives on quality criteria.