Learning to Count Leaves in Rosette Plants

Giuffrida, Mario Valerio; Minervini, Massimo; Tsaftaris, Sotirios

Abstract

Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the \textit{Leaf Counting Challenge} (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ~75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image.

Citation

Giuffrida, M. V., Minervini, M., & Tsaftaris, S. (2015, September). Learning to Count Leaves in Rosette Plants. Presented at Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015, Swansea

• Presentation Conference Type: Edited Proceedings
• Conference Name: Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015
• Start Date: Sep 7, 2015
• End Date: Sep 10, 2015
• Acceptance Date: Jul 24, 2015
• Online Publication Date: Sep 1, 2015
• Publication Date: Sep 1, 2015
• Deposit Date: Sep 23, 2019
• Publicly Available Date: Sep 24, 2019
• Book Title: Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015
• ISBN: 1901725553
• DOI: https://doi.org/10.5244/c.29.cvppp.1
• Public URL: http://researchrepository.napier.ac.uk/Output/2157116