The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation

Data sharing in research is important in order to reproduce results, develop global models, and benchmark methods. This paper presents a dataset containing image and field data from a field plot experiment with oil radish (Raphanus sativus L. var oleiformis) as catch crop after spring barley. The field data consists of fresh weight, dry weight, Carbon content and Nitrogen content from multiple weekly plant samples collected from the plots. The image data consists of images collected weekly prior to the plant samples. A subset of the images corresponding to the plant sampling areas have been annotated pixelwise. In addition to the image and field data, weather data from the growing period is also included in the dataset. The dataset is accompanied by two challenges: 1) semantic segmentation of crops and 2) oil radish yield estimation. The former challenge focuses on data image, while the latter focuses on the field data. Baseline methods and results are provided for both challenges.

[1]  Anders Krogh Mortensen,et al.  Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network , 2016 .

[2]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Buckwell,et al.  The Sustainable Intensification of European Agriculture. , 2014 .

[4]  Rasmus Nyholm Jørgensen,et al.  Weed Growth Stage Estimator Using Deep Convolutional Neural Networks , 2018, Sensors.

[5]  Helge Bonesmo *,et al.  Evaluating an image analysis system for mapping white clover pastures , 2004 .

[6]  Anne Kjersti Bakken,et al.  Spatial and Temporal Abundance of Interacting Populations of White Clover and Grass Species as Assessed by Image Analyses , 2015 .

[7]  Anders Krogh Mortensen,et al.  Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network , 2016 .

[8]  Hanno Scharr,et al.  Finely-grained annotated datasets for image-based plant phenotyping , 2016, Pattern Recognit. Lett..

[9]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[10]  Kim Arild Steen,et al.  Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks , 2017, Sensors.

[11]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[12]  Jörn Ostermann,et al.  A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks , 2014, ECCV Workshops.

[13]  Wolfram Burgard,et al.  Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields , 2017, Int. J. Robotics Res..

[14]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[15]  Rasmus Nyholm Jørgensen,et al.  A Public Image Database for Benchmark of Plant Seedling Classification Algorithms , 2017, ArXiv.