The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation deduction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4. 5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4. 5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.