Andreas Fischbach

Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants.

Stress caused by environmental factors evokes dynamic changes in plant phenotypes. In this study, we deciphered simultaneously the reaction of plant growth and chlorophyll fluorescence related parameters using a novel approach which combines existing imaging technologies (GROWSCREEN FLUORO). Three different abiotic stress situations were investigated demonstrating the benefit of this approach to distinguish between effects related to (1) growth, (2) chlorophyll-fluorescence, or (3) both of these aspects of the phenotype. In a drought stress experiment with more than 500 plants, poly(ADP-ribose) polymerase (PARP) deficient lines of Arabidopsis thaliana (L.) Heynh showed increased relative growth rates (RGR) compared with C24 wild-type plants. In chilling stress, growth of PARP and C24 lines decreased rapidly, followed by a decrease in Fv/Fm. Here, PARP-plants showed a more pronounced decrease of Fv/Fm than C24, which can be interpreted as a more efficient strategy for survival in mild chilling stress. Finally, the reaction of Nicotiana tabacum L. to altered spectral composition of the intercepted light was monitored as an example of a moderate stress situation that affects chlorophyll-fluorescence related, but not growth-related parameters. The examples investigated in this study show the capacity for improved plant phenotyping based on an automated and simultaneous evaluation of growth and photosynthesis at high throughput.

Finely-grained annotated datasets for image-based plant phenotyping

First comprehensive annotated datasets for computer vision tasks in plant phenotyping.Publicly available data and evaluation criteria for eight challenging tasks.Tasks include fine-grained categorization of age, developmental stage, and cultivars.Example test cases and results on plant and leaf-wise segmentation and leaf counting. In this paper we present a collection of benchmark datasets for the development and evaluation of computer vision and machine learning algorithms in the context of plant phenotyping. We provide annotated imaging data and suggest suitable evaluation criteria for plant/leaf segmentation, detection, tracking as well as classification and regression problems. The Figure symbolically depicts the data available together with ground truth segmentations and further annotations and metadata.Display Omitted Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets.

Diel Growth Cycle of Isolated Leaf Discs Analyzed with a Novel, High-Throughput Three-Dimensional Imaging Method Is Identical to That of Intact Leaves

Dicot leaves grow with pronounced diel (24-h) cycles that are controlled by a complex network of factors. It is an open question to what extent leaf growth dynamics are controlled by long-range or by local signals. To address this question, we established a stereoscopic imaging system, GROWSCREEN 3D, which quantifies surface growth of isolated leaf discs floating on nutrient solution in wells of microtiter plates. A total of 458 leaf discs of tobacco (Nicotiana tabacum) were cut at different developmental stages, incubated, and analyzed for their relative growth rates. The camera system was automatically displaced across the array of leaf discs; visualization and camera displacement took about 12 s for each leaf disc, resulting in a time interval of 1.5 h for consecutive size analyses. Leaf discs showed a comparable diel leaf growth cycle as intact leaves but weaker peak growth activity. Hence, it can be concluded that the timing of leaf growth is regulated by local rather than by systemic control processes. This conclusion was supported by results from leaf discs of Arabidopsis (Arabidopsis thaliana) Landsberg erecta wild-type plants and starch-free1 mutants. At night, utilization of transitory starch leads to increased growth of Landsberg erecta wild-type discs compared with starch-free1 discs. Moreover, the decrease of leaf disc growth when exposed to different concentrations of glyphosate showed an immediate dose-dependent response. Our results demonstrate that a dynamic leaf disc growth analysis as we present it here is a promising approach to uncover the effects of internal and external cues on dicot leaf development.

Modeling leaf growth of rosette plants using infrared stereo image sequences

Display Omitted We introduce a novel method for finding and tracking multiple plant leaves.We can automatically measure relevant plant parameters (e.g. leaf growth rates).The procedure has three stages: preprocessing, leaf segmentation, and tracking.The method was tested on infrared tobacco-plant image sequences.The framework was used in a EU project Garnics as a robotic perception unit. In this paper, we present a novel multi-level procedure for finding and tracking leaves of a rosette plant, in our case up to 3 weeks old tobacco plants, during early growth from infrared-image sequences. This allows measuring important plant parameters, e.g. leaf growth rates, in an automatic and non-invasive manner. The procedure consists of three main stages: preprocessing, leaf segmentation, and leaf tracking. Leaf-shape models are applied to improve leaf segmentation, and further used for measuring leaf sizes and handling occlusions. Leaves typically grow radially away from the stem, a property that is exploited in our method, reducing the dimensionality of the tracking task. We successfully tested the method on infrared image sequences showing the growth of tobacco-plant seedlings up to an age of about 30days, which allows measuring relevant plant growth parameters such as leaf growth rate. By robustly fitting a suitably modified autocatalytic growth model to all growth curves from plants under the same treatment, average plant growth models could be derived. Future applications of the method include plant-growth monitoring for optimizing plant production in green houses or plant phenotyping for plant research.

phenoSeeder - A robot system for automated handling and phenotyping of individual seeds

A robot system allows automated handling and phenotyping of individual seeds of different sizes, delivering biometric traits relevant for various aspects in seed biology. The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.

3D Surface Reconstruction of Plant Seeds by Volume Carving: Performance and Accuracies

We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, theoretically achievable accuracy, experimentally achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes.

3D Surface Reconstruction of Plant Seeds by Volume Carving

We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, the achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes.

A cognitive architecture for automatic gardening

A cognitive system to autonomously control the growth of plants is proposed.The system integrates artificial intelligence and robotic techniques.Decisions are made using symbolic planning and machine learning.Plants are modelled using 3D model acquisition of deformable objects (leaves).Action rules are learned during run-time under the guidance of a human gardener. In large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual needs should be applied. Experienced human gardeners are very good at treating plants individually. Unfortunately, hiring a crew of gardeners to carry out this task in large greenhouses is not cost effective. In this work we present a cognitive system that integrates artificial intelligence (AI) techniques for decision-making with robotics techniques for sensing and acting to autonomously treat plants using a real-robot platform. Artificial intelligence techniques are used to decide the amount of water and nutrients each plant needs according to the history of the plant. Robotic techniques for sensing measure plant attributes (e.g. leaves) from visual information using 3D model representations. These attributes are used by the AI system to make decisions about the treatment to apply. Acting techniques execute robot movements to supply the plants with the specified amount of water and nutrients.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called “Octree” with an efficient image region integration scheme called “Integral image.” Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy “Mark and refine.” Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.