Muroni, Wahyu Imam (2025) RASPBERRY Pi BASED ON MANGO PICKER FOR RIPENESS DETECTION USING K-NN METHOD. Skripsi thesis, Universitas Pakuan.
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RASPBERRY Pi BASED ON MANGO PICKER FOR RIPENESS DETECTION USING K-NN METHOD Wahyu Imam Muhroni1 , Asep Denih2 , Teguh Pujanegara3 , Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Email: [email protected] Abstract Mango harvesting in Indonesia is still widely carried out manually, which may lead to inaccuracies in determining fruit ripeness and increase the risk of fruit damage during the harvesting process. This study aims to design and develop a prototype of an automatic mango fruit picking tool capable of identifying fruit ripeness using the K-Nearest Neighbor (K-NN) method based on Raspberry Pi. The system integrates Raspberry Pi as the main controller, Pi Camera as the visual sensor, OpenCV for image processing, the KNN algorithm for ripeness classification, and a servo motor as the picking actuator. Mango images captured by the camera are processed to obtain the average RGB values from a specific Region of Interest (ROI). These RGB values are then compared with training data using Euclidean Distance to classify the fruit into three ripeness categories: unripe, half-ripe, and ripe. The classification result is used as the decision-making basis, where the servo motor is activated only when the fruit is detected as ripe. Based on the testing results, the system is able to identify mango ripeness in real time and activate the actuator according to the classification output. However, system performance is still affected by lighting conditions, camera distance, object position, and the limited size of the dataset. Therefore, this tool can serve as an initial prototype for applying computer vision and machine learning technology to support more selective and efficient automated mango harvesting. Keywords: K-Nearest Neighbor, Raspberry Pi, Mango Fruit Picker, Ripeness Classification, RGB, Computer Vision, Servo Motor
| Item Type: | Thesis (Skripsi) |
|---|---|
| Subjects: | Fakultas Ilmu Pengetahuan Alam dan Matematika > Ilmu Komputer |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Ilmu Komputer |
| Depositing User: | PERPUSTAKAAN FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNPAK |
| Date Deposited: | 08 Jul 2026 07:49 |
| Last Modified: | 08 Jul 2026 07:49 |
| URI: | http://eprints.unpak.ac.id/id/eprint/10816 |
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