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RSS FeedsRemote Sensing, Vol. 11, Pages 691: Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network (Remote Sensing)


22 march 2019 11:01:51

Remote Sensing, Vol. 11, Pages 691: Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network (Remote Sensing)

The number of rice seedlings in the field is one of the main agronomic components for determining rice yield. This counting task, however, is still mainly performed using human vision rather than computer vision and is thus cumbersome and time-consuming. A fast and accurate alternative method of acquiring such data may contribute to monitoring the efficiency of crop management practices, to earlier estimations of rice yield, and as a phenotyping trait in breeding programs. In this paper, we propose an efficient method that uses computer vision to accurately count rice seedlings in a digital image. First, an unmanned aerial vehicle (UAV) equipped with red-green-blue (RGB) cameras was used to acquire field images at the seedling stage. Next, we use a regression network (Basic Network) inspired by a deep fully convolutional neural network to regress the density map and estimate the number of rice seedlings for a given UAV image. Finally, an improved version of the Basic Network, the Combined Network, is also proposed to further improve counting accuracy. To explore the efficacy of the proposed method, a novel rice seedling counting (RSC) dataset was built, which consisted of 40 images (where the number of seedlings varied between 3732 and 16,173) and corresponding manually-dotted annotations. The results demonstrated high average accuracy (higher than 93%) between counts according to the proposed method and manual (UAV image-based) rice seedling counts, and very good performance, with a high coefficient of determination (R2) (around 0.94). In conclusion, the results indicate that the proposed method is an efficient alternative for large-scale counting of rice seedlings, and offers a new opportunity for yield estimation. The RSC dataset and source code are available online. Digg Facebook Google StumbleUpon Twitter
25 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 680: CARIB18: A Stable Geodetic Reference Frame for Geological Hazard Monitoring in the Caribbean Region (Remote Sensing)
Remote Sensing, Vol. 11, Pages 690: Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data (Remote Sensing)
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