Machine learning algorithm for Avocado image segmentation based on quantum enhancement and Random forest

Amraoui, Khalid El; Ezzaki, Ayoub; Masmoudi, Lhoussaine; Hadri, Majid; Belrhiti, Hicham El; Ansari, Mohamed El; Amari, A.

doi:10.1109/iraset52964.2022.9738360

Cited by 4 publications

(1 citation statement)

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“…segmentation. And finally, the work presented in [8], [9], use quantum superposition laws and machine learning for agricultural image segmentation…”

mentioning

confidence: 99%

An algorithm for crops segmentation in UAV images based on U-Net CNN model: Application to Sugarbeets plants

et al. 2022

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In recent years, Digital Agriculture (DA) has been widely developed using new technologies and computer vision technics. Drones and Machine learning have proved their efficiency in the optimization of the agricultural management. In this paper we propose an algorithm based on U-Net CNN Model to crops segmentation in UAV images. The algorithm patches the input images into several 256×256 sub-images before creating a mask (ground-truth) that will be fed into a U-Net Model for training. A set of experimentation has been done on real UAV images of Sugerbeets crops, where the mean intersection over Union (MIoU) and the Segmentation accuracy (SA) metrics are adopted to evaluate its performances against other algorithms used in the literature. The proposed algorithm show a good segmentation accuracy compared to three well-known algorithms for UAV image segmentation.

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“…segmentation. And finally, the work presented in [8], [9], use quantum superposition laws and machine learning for agricultural image segmentation…”

mentioning

confidence: 99%