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Article

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

Department of Architecture, Design & Media Technology, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
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Received: 1 July 2019 / Revised: 6 August 2019 / Accepted: 7 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)
Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the adoption of two deep learning-based methods for kernel processing prediction without the cumbersome step of separating kernels and stover before capturing images. The methods show that kernels can be detected both with bounding boxes and at pixel-level instance segmentation. Networks were trained on up to 1393 images containing just over 6907 manually annotated kernel instances. Both methods showed promising results despite the challenging setting, with an average precision at an intersection-over-union of 0.5 of 34.0% and 36.1% on the test set consisting of images from three different harvest seasons for the bounding-box and instance segmentation networks respectively. Additionally, analysis of the correlation between the Kernel Processing Score (KPS) of annotations against the KPS of model predictions showed a strong correlation, with the best performing at r(15) = 0.88, p = 0.00003. The adoption of deep learning-based object recognition approaches for kernel processing measurement has the potential to lower the quality assessment process to minutes, greatly aiding a farmer in the strenuous harvesting season. View Full-Text
Keywords: deep learning; object recognition; precision agriculture; silage; kernel processing; forage deep learning; object recognition; precision agriculture; silage; kernel processing; forage
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MDPI and ACS Style

Rasmussen, C.B.; Moeslund, T.B. Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images. Sensors 2019, 19, 3506. https://0-doi-org.brum.beds.ac.uk/10.3390/s19163506

AMA Style

Rasmussen CB, Moeslund TB. Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images. Sensors. 2019; 19(16):3506. https://0-doi-org.brum.beds.ac.uk/10.3390/s19163506

Chicago/Turabian Style

Rasmussen, Christoffer B., and Thomas B. Moeslund 2019. "Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images" Sensors 19, no. 16: 3506. https://0-doi-org.brum.beds.ac.uk/10.3390/s19163506

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