Artificial intelligence approach for tomato detection and mass estimation in precision agriculture
Abstract
Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.
Citation
Lee , J , Nazki , H , Baek , J , Hong , Y & Lee , M 2020 , ' Artificial intelligence approach for tomato detection and mass estimation in precision agriculture ' , Sustainability , vol. 12 , no. 21 , e9138 . https://doi.org/10.3390/su12219138
Publication
Sustainability
Status
Peer reviewed
ISSN
2071-1050Type
Journal article
Rights
Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Description
Funding: This study was carried out with the support of “Research Program for Agricultural Science & Technology Development” (Project No: PJ013891012020), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.Collections
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