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Article

A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

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Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
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School of Electronic Engineering, Changzhou College of Information Technology, Changzhou 213164, China
*
Author to whom correspondence should be addressed.
Academic Editor: Antonello Bonfante
Agriculture 2021, 11(10), 997; https://doi.org/10.3390/agriculture11100997
Received: 15 September 2021 / Revised: 8 October 2021 / Accepted: 10 October 2021 / Published: 13 October 2021
(This article belongs to the Section Digital Agriculture)
Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation. View Full-Text
Keywords: precision agriculture; deep learning; semantic segmentation; grape segmentation precision agriculture; deep learning; semantic segmentation; grape segmentation
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MDPI and ACS Style

Peng, Y.; Wang, A.; Liu, J.; Faheem, M. A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties. Agriculture 2021, 11, 997. https://doi.org/10.3390/agriculture11100997

AMA Style

Peng Y, Wang A, Liu J, Faheem M. A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties. Agriculture. 2021; 11(10):997. https://doi.org/10.3390/agriculture11100997

Chicago/Turabian Style

Peng, Yun, Aichen Wang, Jizhan Liu, and Muhammad Faheem. 2021. "A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties" Agriculture 11, no. 10: 997. https://doi.org/10.3390/agriculture11100997

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