System of Counting Green Oranges Directly from Trees Using Artificial Intelligence
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Published:2023-10-09
Issue:4
Volume:5
Page:1813-1831
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ISSN:2624-7402
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Container-title:AgriEngineering
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language:en
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Short-container-title:AgriEngineering
Author:
Gremes Matheus Felipe1, Fermo Igor Rossi1ORCID, Krummenauer Rafael1ORCID, Flores Franklin César2, Andrade Cid Marcos Gonçalves1ORCID, Lima Oswaldo Curty da Motta1
Affiliation:
1. Department of Chemical Engineering, State University of Maringá (UEM), Maringá 87020-900, PR, Brazil 2. IT Department, State University of Maringá (UEM), Maringá 87020-900, PR, Brazil
Abstract
Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from trees through video footage filmed in line along a row of orange trees on a plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object-tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. Being the scientific and technological innovation the possibility of distinguishing the green color of the fruits from the green color of the leaves. The use of YOLOv4 together with object detectors managed to reduce the number of double counting errors and obtained a count close to the actual number of oranges visible in the video. The results were promising, with an mAP50 of 80.16%, mAP50:95 of 53.83%, precision of 0.92, recall of 0.93, F1-score of 0.93, and average IoU of 82.08%. Additionally, the counting algorithm successfully identified and counted 204 oranges, closely approaching the actual count of 208. The study also resulted in a database with an amount of 644 images containing 43,109 orange annotations that can be used in future works.
Funder
National Council for Scientific and Technological Development National Council for the Improvement of Higher Education
Subject
Engineering (miscellaneous),Horticulture,Food Science,Agronomy and Crop Science
Reference51 articles.
1. Abdullahi, H.S., Sheriff, R., and Mahieddine, F. (2017, January 16–18). Convolution neural network in precision agriculture for plant image recognition and classification. Proceedings of the 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, UK. 2. Pre-Harvest Fruit Image Processing: A Brief Review;Gremes;Braz. J. Exp. Des. Data Anal. Inferent. Stat.,2021 3. On plant detection of intact tomato fruits using image analysis and machine learning methods;Yamamoto;Sensors,2014 4. Wang, Q., Nuske, S., Bergerman, M., and Singh, S. (2013). Experimental Robotics, Proceedings of the 13th International Symposium on Experimental Robotics, Québec City, QC, Canada, 18–21 June 2012, Springer. 5. Zhang, Q., Liu, Y., Gong, C., Chen, Y., and Yu, H. (2020). Applications of deep learning for dense scenes analysis in agriculture: A review. Sensors, 20.
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