Fruits and Vegetables Recognition using YOLO

• Published in: 2022 International Conference on Computer Communication and Informatics (ICCCI) pp. 1 - 6
• Main Authors: Latha, R.S., Sreekanth, G.R., Rajadevi, R., Nivetha, S.K., Kumar, K.Ajith, Akash, V., Bhuvanesh, S., Anbarasu, Pon
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.01.2022
• Subjects: Computational modeling; Convolutional Neural Network; Deep learning; Detectors; Fruits and Vegetable detection; Object detection; Performance evaluation; Predictive models; Real-time systems; Recognition; Streaming media; YOLO; YOLOv4 Tiny
• DOI: 10.1109/ICCCI54379.2022.9740820

Real-time live detection of fruits and vegetables is the most important task to know the availability of the current stock of fruits and vegetables that the customers looking for in the vegetable market. For this problem, a new technique based on Deep learning and IoT is required. The proposed work has applied the YOLO model for identifying different types of vegetables and fruits available in the vegetable market and hence the customer can know the updates the livestock of fruits and vegetables in that shop. The images of commonly used fruits and vegetables in India are collected from Google and also from Kaggle. The collected images are labeled using the Roboflow framework. To implement the proposed model, YOLOv4 -tiny model is chosen which is a super-fast object detector with better accuracy and is also most suitable for embedded devices. Further, this tiny model performs fast prediction in real-time video processing scenarios. The result of the proposed system is evaluated using the metric mean Average Precision (mAP). A high-value of mAP indicates that the model performs better is in its detection. Our YOLOv4-tiny model produced an mAP value of 51% with an inference time of 18 milliseconds.