Spike Detection and Segmentation Tool - SpikeApp v0.1

Introduction

SpikeApp tool was developed to demonstrate performance of three of totally six shallow and deep learning neural network models (ANN, YOLO, U-Net) for detection and segmentation of grain spikes in greenhouse images published in [1].

Key Features

The SpikeApp tool performs fully automated detection/segmentation of spikes of wheat/barley/rye spikes or one single image at a time per user click. Subsequently, segmented root structures of each single image are quantitatively characterized in terms of various traits including geometric and color features.

Downloads

download Windows x64 (350MB)
download Linux (350MB)

By downloading, installing and/or using this software the user agrees with the following license conditions.

This software requires MATLAB run time libraries (2020a) to be installed:

MATLAB run time for Windows x64 (~2GB).
MATLAB run time for Linux (~2GB).

Some information on dependencies of the MATLAB run-time components in Linux can be found here: https://de.mathworks.com/matlabcentral/answers/358052-is-there-a-list-of-matlab-runtime-dependencies.

General hardware requirements for MATLAB applications can be found here: https://de.mathworks.com/support/requirements/matlab-system-requirements.html.

Quick Start

For install notes and further informations, please see the SpikeApp manual included with this distribution (SpikeApp.pdf).

References

[1] Ullah S, Henke M, Narisetti N, Panzarová K, Trtílek M, Hejatko J, Gladilin E. Towards automated analysis of grain spikes in greenhouse images using neural network approaches: a comparative investigation of six methods. Sensors 21 (2021) 7441. https://dx.doi.org/10.3390/s21227441

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