Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms - Rachmad, Iqbal; Tri Basuki, Kurniawan; Misinem, .; Edi Surya, Negara; Tata, Sutabri (INTI International University, 2024)
Manual selection of fresh fruit has been identified as a significant challenge for the agricultural sector due to its time-consuming nature and potential for inconsistent categorization. This process requires human labour to visually inspect and sort fruits, leading to variability and inefficiencies in the sorting process. This research proposes a low-cost alternative using intelligent fruit selection systems based on computer vision techniques to address these issues. These systems aim to automate the process of fruit selection, improving efficiency and consistency in categorizing fruits such as apples, bananas, and oranges. A critical step in developing such intelligent systems is the feature extraction process. Feature extraction is essential in classification, especially for data sources in the form of images. It involves identifying and isolating relevant information from the images that classification algorithms can use to distinguish between different fruit categories. If the feature extraction process fails to capture the correct information, the performance or accuracy of the classification algorithm will be negatively impacted. This research compares three different methods for extracting features from fruit images to determine which method yields the highest accuracy for fruit classification. The feature extraction methods evaluated were Grayscale Pixel Values, Mean Pixel Value of Channels, and Extracting Edge Features. The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. CNNs are well-suited for image classification tasks due to their ability to learn hierarchical feature representations from the input images automatically. By comparing the performance of the CNN classifier using the three different feature extraction methods, this research aims to identify the method that provides the highest level of accuracy in classifying fruit images. By conducting this comparative analysis, the research provides insights into the most effective feature extraction techniques for improving the performance of computer vision-based fruit selection systems, ultimately contributing to more efficient and consistent fruit categorization in the agricultural sector. The result revealed that the Grayscale achieved the highest validation accuracy (99.94%) and the lowest validation loss (0.44%).