Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Tsaftaris, Sotirios A.; Noutsos, Christos

doi:10.1007/978-3-540-88351-7_18

Cited by 38 publications

(40 citation statements)

References 20 publications

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“…Figure 4) consisted of a growth shelf and an automated affordable sensing system (Tsaftaris and Noutsos, 2009), to acquire and send images of the scene via a wireless connection to a receiving computer (http://www. phenotiki.com).…”

Section: Arabidopsis Imaging With An Affordable Settingmentioning

confidence: 99%

Finely-grained annotated datasets for image-based plant phenotyping

Minervini

Fischbach

Scharr

et al. 2016

Pattern Recognition Letters

244

209

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Graphical AbstractFinely-grained annotated datasets for image-based plant phenotyping Massimo Minervini, Andreas Fischbach, Hanno Scharr, Sotirios A. TsaftarisIn 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. Research Highlights• 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. ABSTRACTImage-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.

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Section: Arabidopsis Imaging With An Affordable Settingmentioning

confidence: 99%

Finely-grained annotated datasets for image-based plant phenotyping

Minervini

Fischbach

Scharr

et al. 2016

Pattern Recognition Letters

244

209

View full text Add to dashboard Cite

show abstract

“…The use of off-the-shelf commercial equipment (such as commercial cameras [12] or the Kinect [5]) could facilitate standardization across experiments, lower the entry barrier, offer affordable solutions, and help many labs adopt the image-based approach to plant phenotyping.…”

Section: Affordability: Coping With Restrictionsmentioning

confidence: 99%

Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

Minervini

Scharr

Tsaftaris

2015

IEEE Signal Process. Mag.

213

118

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“…The combination of low-cost smart sensors with Internet connectivity [3] and a cloud infrastructure (e.g., the iPlant Collaborative project (http://www.iplantcollaborative.org/)) can mitigate the above limitations. To keep the cost of the sensor low, minimal robotics and minimal computational power is assumed, with the bulk of analysis occurring at remote infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Application-aware image compression for low cost and distributed plant phenotyping

Minervini

Tsaftaris

2013

2013 18th International Conference on Digital Signal Processing (DSP)

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Plant phenotyping investigates how a plant's genome, interacting with the environment, affects the observable traits of a plant (phenome). It is becoming increasingly important in our quest towards efficient and sustainable agriculture. While sequencing the genome is becoming increasingly efficient, acquiring phenotype information has remained largely of low throughput, since high throughput solutions are costly and not widespread. A distributed approach could provide a low cost solution, offering high accuracy and throughput. A sensor of low computational power acquires time-lapse images of plants and sends them to an analysis system with higher computational and storage capacity (e.g., a service running on a cloud infrastructure). However, such system requires the transmission of imaging data from sensor to receiver, which necessitates their lossy compression to reduce bandwidth requirements. In this paper, we propose an application aware image compression approach where the sensor is aware of its context (i.e., imaging plants) and takes advantage of the feedback from the receiver to focus bitrate on regions of interest (ROI). We use JPEG 2000 with ROI coding, and thus remain standard compliant, and offer a solution that is low cost and has low computational requirements. We evaluate our solution in several images of Arabidopsis thaliana phenotyping experiments, and we show that both for traditional metrics (such as PSNR) and application aware metrics, the performance of the proposed solution provides a 70% reduction of bitrate for equivalent performance.

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Plant Phenotyping with Low Cost Digital Cameras and Image Analytics

Cited by 38 publications

References 20 publications

Finely-grained annotated datasets for image-based plant phenotyping

Finely-grained annotated datasets for image-based plant phenotyping

Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

Application-aware image compression for low cost and distributed plant phenotyping

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