Eggplant Variety Classification Using Image Processing

Abstract

Image processing is a powerful technique used in various fields, including agriculture, to enhance images and extract valuable information. This study focuses on utilizing image processing to determine crop parameters such as area, perimeter, and volume, thereby facilitating the assessment of crop quality. The integration of fuzzy logic concepts and the k-nearest neighbors (KNN) classifier further enhances image processing outcomes. The objective of the study was to identify eggplant varieties based on seed analysis using image processing, fuzzy logic, and KNN algorithms. The methodology involved enhancing image quality and extracting essential features from eggplant seeds, including area, perimeter, equivalent diameter, and roundness, which are used for subsequent fuzzy logic and KNN classification. The results demonstrate a successful application of image enhancement and feature extraction on eggplant seed images. The proposed approach accurately identified eggplant varieties using fuzzy logic and KNN algorithms. In conclusion, the extracted features are crucial for the classification process. Future research should expand the dataset to include a wider range of eggplant varieties, explore alternative machine-learning techniques, and consider variations in lighting conditions and seed sizes. These recommendations aim to improve the accuracy and robustness of the classification model. This research holds promise for broader applications in agriculture, crop classification, plant disease detection, and quality control in food production. The integration of image processing, fuzzy logic, and KNN presents valuable opportunities for advancements in various industries.