Application of spectral reflectance for increasing plant discrimination speed in precision agriculture

Author Identifier

Saman Akbarzadeh
ORCID: 0000-0003-4293-1797

Document Type

Conference Proceeding

Publication Title

2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

School

School of Science / Electron Science Research Institute

RAS ID

30304

Comments

Akbarzadeh, S., Ahderom, S., & Alameh, K. (2019). Application of spectral reflectance for increasing plant discrimination speed in precision agriculture. In Proceedings of the 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) (pp. 140-142). Available here

Abstract

Increasing the speed of weed/crop discrimination sensor engines is an increasingly challenging research area in precision agriculture (PA). Data collection, modelling, and real-Time operation are currently the major challenges for accurate plant classification and effective weed control. In the current study, a new low-resolution spectral reflectance sensing is proposed for data collection and applied in conjunction with state-of-Art convolutional neural network (CNN) algorithm for real-Time weed detection. Experimental results demonstrate that the speed of the algorithm is ten times faster than typical spatial imaging based counterparts, while its discrimination accuracy is almost the same.

DOI

10.1109/HONET.2019.8907994

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