@article{eprints3606,
            note = {Available online 31 October 2016},
       publisher = {Elsevier},
         journal = {Trends in Plant Science},
          author = {Sotirios A. Tsaftaris and Massimo Minervini and Hanno Scharr},
           month = {October},
          volume = {21},
          number = {12},
           pages = {989--991},
           title = {Machine Learning for Plant Phenotyping Needs Image Processing},
            year = {2016},
             url = {http://eprints.imtlucca.it/3606/},
        abstract = {We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2].},
        keywords = {image processing, machine learning, plant phenotyping, stress}
}