Abstract
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.
Highlights
Grapes are one of the most favorite fruits in the world
The color of the images looks distorted and unnatural. This may be due to the irreducible singularity of the transformation between RGB and HSI space, and the fact that in this work Histogram Equalization (HE) was only implemented on the intensity channel
The performance obtained in our experiment indicate the deep learning related method shows huge potential for grape cluster segmentation especially for grapes with different varieties
Summary
Grapes are one of the most favorite fruits in the world. The harvested grapes are used for winemaking or fresh food. For grapes used for winemaking, there is no need to consider the shedding of grape berries and the damage to the clusters during the picking process, which is more suitable for non-selective mechanized picking methods. For the picking of table grapes, it is necessary to consider that there may be fruit loss and berries damaged during the picking process while completing the harvest of all mature grapes in the vineyard. The large-scale non-selective mechanical picking method is not suitable for the harvest of table grapes. The harvesting of table grapes is often done manually, which is a labor-intensive and time-consuming work [4]. With the development of robot technology, the best strategy to solve this problem is to use robots instead of farmers to harvest table grapes manually
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