Automated Apple Recognition System Using Semantic Segmentation Networks with Group and Shuffle Operators

Mohd Asyraf Zulkifley, Asraf Mohamed Moubark, Adhi Harmoko Saputro and Siti Raihanah Abdani
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Mohd Asyraf Zulkifley: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Asraf Mohamed Moubark: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Adhi Harmoko Saputro: Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, Indonesia
Siti Raihanah Abdani: Faculty of Humanities, Management and Science, Universiti Putra Malaysia (Bintulu Campus), Bintulu 97008, Sarawak, Malaysia

Agriculture, 2022, vol. 12, issue 6, 1-15

Abstract: Apples are one of the most consumed fruits, and they require efficient harvesting procedures to remains in optimal states for a longer period, especially during transportation. Therefore, automation has been adopted by many orchard operators to help in the harvesting process, which includes apple localization on the trees. The de facto sensor that is currently used for this task is the standard camera, which can capture wide view information of various apple trees from a reasonable distance. Therefore, this paper aims to produce the output mask of the apple locations on the tree automatically by using a deep semantic segmentation network. The network must be robust enough to overcome all challenges of shadow, surrounding illumination, size variations, and occlusion to produce accurate pixel-wise localization of the apples. A high-resolution deep architecture is embedded with an optimized design of group and shuffle operators (GSO) to produce the best apple segmentation network. GSO allows the network to reduce the dependency on a few sets of dominant convolutional filters by forcing each smaller group to contribute effectively to the task of extracting optimal apple features. The experimental results show that the proposed network, GSHR-Net, with two sets of group convolution applied to all layers produced the best mean intersection over union of 0.8045. The performance has been benchmarked with 11 other state-of-the-art deep semantic segmentation networks. For future work, the network performance can be increased by integrating synthetic augmented data to further optimize the training phase. Moreover, spatial and channel-based attention mechanisms can also be explored by emphasizing some strategic locations of the apples, which makes the recognition more accurate.

Keywords: apples recognition; deep learning; semantic segmentation; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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