Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting
Ryan Joshua Liwag1, Kevin Jeff Cepria2, Anfernee Rapio3, Karlos Leo Castillo4, Melvin Cabatuan5
1Ryan Joshua Liwag, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
2Kevin Jeff Cepria, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
3Anfernee Rapio, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
4Karlos Leo Castillo, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
5Melvin Cabatuan, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
Manuscript received on 15 March 2019 | Revised Manuscript received on 21 March 2019 | Manuscript published on 30 July 2019 | PP: 5456-5462 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3754078219/19©BEIESP | DOI: 10.35940/ijrte.B3754.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents the design and development of an embedded system for ‘Carabao’ or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques.
Index Terms: Deep Learning, Embedded System, Deep Artificial Neural Network, Multi-Task Learning, Region-Based Convolutional Neural Network, Image-Based Mango Sorting.
Scope of the Article: Deep Learning