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

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.

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Field Value
Author Alkhudaydi, Tahani
Last Updated November 20, 2019, 16:52 (UTC)
Created November 4, 2019, 17:57 (UTC)
Article Is Open Access false
Citation Report https://scite.ai/reports/10.1007/978-3-030-20912-4_1
DOI 10.1007/978-3-030-20912-4_1
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Journal Is Open Access false
Open Access Status closed