Preprint Article Version 1 This version is not peer-reviewed

A System for Weeds and Crops Identification – Reaching over 10 FPS on Raspberry Pi with the Usage of MobileNets, DenseNet and Custom Modifications.

Version 1 : Received: 5 July 2019 / Approved: 8 July 2019 / Online: 8 July 2019 (12:29:21 CEST)

How to cite: Chechliński, Ł.; Siemiątkowska, B.; Majewski, M. A System for Weeds and Crops Identification – Reaching over 10 FPS on Raspberry Pi with the Usage of MobileNets, DenseNet and Custom Modifications.. Preprints 2019, 2019070115 (doi: 10.20944/preprints201907.0115.v1). Chechliński, Ł.; Siemiątkowska, B.; Majewski, M. A System for Weeds and Crops Identification – Reaching over 10 FPS on Raspberry Pi with the Usage of MobileNets, DenseNet and Custom Modifications.. Preprints 2019, 2019070115 (doi: 10.20944/preprints201907.0115.v1).

Abstract

Automated weeding is an important research area in agrorobotics. Weeds can be removed mechanically or with the precise usage of herbicides. Deep Learning techniques achieved state of the art results in many computer vision tasks, however their deployment on low-cost mobile computers is still challenging. These paper present an advanced version of the system presented in [1]. The described system contains several novelties, compared both with its previous version and related work. It is a part of a project of the automatic weeding machine, developed by Warsaw University of Technology and MCMS Warka Ltd. The obtained model reaches satisfying accuracy at over 10~FPS on the Raspberry Pi 3B+ computer. It was tested for four different plant species at different growth stadiums and lighting conditions. The system performing semantic segmentation is based on Convolutional Neural Networks. Its custom architecture mixes U-Net, MobileNets, DenseNet and ResNet concepts. Amount of needed manual ground truth labels was significantly decreased by the usage of knowledge distillation process, learning final model to mimic an ensemble of complex models on the large database of unlabeled data. Further decrease of the inference time was obtained by two custom modifications: in the usage of separable convolutions in DenseNet block and in the number of channels in each layer. In the authors’ opinion, described novelties can be easily transferred to other agrorobotics tasks.

Subject Areas

Automated Weeding; Mobile Convolutional Neural Netowrks, Semantic Segmentation

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