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      A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

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          Abstract

          X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

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          Most cited references61

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Fully convolutional networks for semantic segmentation

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              Adam: A Method for Stochastic Optimization

              We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                13 September 2022
                2022
                : 13
                : 893140
                Affiliations
                [1] 1Horticultural Crops Production and Genetic Improvement Research Unit-United States Department of Agriculture-Agricultural Research Service , Prosser, WA, United States
                [2] 2Department of Biological and Agricultural Engineering, University of California, Davis , Davis, CA, United States
                [3] 3Department of Viticulture and Enology, University of California, Davis , Davis, CA, United States
                [4] 4Department of Computer Science, California Polytechnic and State University, San Luis Obispo , CA, United States
                [5] 5Department of Integrative Biology, San Francisco State University , San Francisco, CA, United States
                [6] 6Advanced Light Source, Lawrence Berkeley National Laboratory , Berkeley, CA, United States
                [7] 7Department of Plant Sciences, University of California, Davis , Davis, CA, United States
                [8] 8Genetic Improvement for Fruits and Vegetables Laboratory, United States Department of Agriculture-Agricultural Research Service , Chatsworth, NJ, United States
                [9] 9Crops Pathology and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service , Davis, CA, United States
                Author notes

                Edited by: Dirk Walther, Max Planck Institute of Molecular Plant Physiology, Germany

                Reviewed by: Deepak Sinwar, Manipal University Jaipur, India; Craig J. Sturrock, University of Nottingham, United Kingdom

                *Correspondence: Devin A. Rippner, devin.rippner@ 123456usda.gov

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.893140
                9514790
                36176692
                734f3a76-19d4-4bd9-b69e-9f21452af64e
                Copyright © 2022 Rippner, Raja, Earles, Momayyezi, Buchko, Duong, Forrestel, Parkinson, Shackel, Neyhart and McElrone.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 March 2022
                : 12 August 2022
                Page count
                Figures: 7, Tables: 0, Equations: 4, References: 62, Pages: 13, Words: 7401
                Funding
                Funded by: U.S. Department of Energy, doi 10.13039/100000015;
                Award ID: DE-AC02- 05CH11231
                Funded by: Agricultural Research Service, doi 10.13039/100007917;
                Award ID: 2072-21000-057-000-D
                Award ID: 2032-21220-008-000-D
                Categories
                Plant Science
                Methods

                Plant science & Botany
                x-ray computed tomography,deep learning,machine learning and ai,soil science,plant science,soil aggregate analysis,soil health,plant physiology

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