Designing Future Precision Agriculture: Detection of Seeds Germination Using Artificial Intelligence on a Low-Power Embedded System

Dmitrii Shadrin, Alexander Menshchikov, Dmitry Ermilov, Andrey Somov

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)

    Abstract

    Artificial Intelligence (AI) has been recently applied to a number of sensing scenarios for realizing the prediction, control and/or recognition tasks. However, its integration to embedded systems is still limited. We propose a low-power sensing system with the AI on board with a special focus on the application in agriculture. For this reason we designed a Convolutional Neural Network (CNN) which achieves 83% of average Intersection over Union (IoU) score on the test dataset and 97% of seeds recognition accuracy on the validation dataset. The proposed solution is able to perform the seeds recognition, and germination detection through the images processing. For training the CNN we collect a dataset of images of seed germination process at different stages. The entire system is assessed in an industrial facility. The experimental results demonstrate that the proposed system opens up wide vista for smart applications in the context of Internet of Things requiring the intelligent and autonomous operation from 'things'.

    Original languageEnglish
    Article number8804220
    Pages (from-to)11573-11582
    Number of pages10
    JournalIEEE Sensors Journal
    Volume19
    Issue number23
    DOIs
    Publication statusPublished - 1 Dec 2019

    Keywords

    • artificial intelligence
    • computer vision
    • embedded sensing
    • machine learning
    • smart agriculture
    • Smart sensing

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