SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks

Alkhudaydi, Tahani, Zhou, Ji and De La Iglesia, Beatriz (2019) SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks. In: Lecture Notes in Computing Science. Artificial Intelligence and Soft Computing . Springer, pp. 3-13. ISBN 978-3-030-20911-7

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Abstract

Currently, crop management through automatic monitoring is growing momentum, but presents various challenges. One key challenge is to quantify yield traits from images captured automatically. Wheat is one of the three major crops in the world with a total demand expected to exceed 850 million tons by 2050. In this paper we attempt estimation of wheat spikelets from high-definition RGB infield images using a fully convolutional model. We propose also the use of transfer learning and segmentation to improve the model. We report cross validated Mean Absolute Error (MAE) and Mean Square Error (MSE) of 53.0, 71.2 respectively on 15 real field images. We produce visualisations which show the good fit of our model to the task. We also concluded that both transfer learning and segmentation lead to a very positive impact for CNN-based models, reducing error by up to 89%, when extracting key traits such as wheat spikelet counts.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 30 May 2019 13:30
Last Modified: 08 Aug 2020 23:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/71173
DOI: 10.1007/978-3-030-20912-4_1

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