398 research outputs found
Opportunities and limitations of crop phenotyping in southern european countries
ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts.
Mediterranean crops are frequently subjected to high evapotranspiration demands,
soil water deficits, high temperatures, and photo-oxidative stress. These conditions
will become more severe due to global warming which poses major challenges to the
sustainability of the agricultural sector in Mediterranean countries. Selection of crop
varieties adapted to future climatic conditions and more tolerant to extreme climatic events
is urgently required. Plant phenotyping is a crucial approach to address these challenges.
High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved
genotypes and is one of the most effective strategies to improve the sustainability of
agricultural production. In spite of the remarkable progress in basic knowledge and
technology of plant phenotyping, there are still several practical, financial, and political
constraints to implement HTPP approaches in field and controlled conditions across the
Mediterranean. The European panorama of phenotyping is heterogeneous and integration
of phenotyping data across different scales and translation of “phytotron research” to the
field, and from model species to crops, remain major challenges. Moreover, solutions
specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses)
are in high demand, as the region is vulnerable to climate change and to desertification
processes. The specific phenotyping requirements of Mediterranean crops have not
yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor,
though the limited availability of skilled personnel may also impair its implementation in
Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures
is hindering the development of new Mediterranean agricultural varieties and will negatively
affect future competitiveness of the agricultural sector. We provide an overview of the
heterogeneous panorama of phenotyping within Mediterranean countries, describing the
state of the art of agricultural production, breeding initiatives, and phenotyping capabilities
in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify
strategies to overcome barriers and maximize the benefits of phenotyping and modeling
approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio
Learning to Count Leaves in Rosette Plants
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
A Segmentation-Guided Deep Learning Framework for Leaf Counting
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes
LiDARPheno: A Low-Cost LiDAR-based 3D Scanning System for Plant Morphological Trait Characterization
The ever-growing world population brings the challenge for food security in the current world. The gene modification tools have opened a new era for fast-paced research on new crop identification and development. However, the bottleneck in the plant phenotyping technology restricts the alignment in Geno-pheno development as phenotyping is the key for the identification of potential crop for improved yield and resistance to the changing environment. Various attempts to making the plant phenotyping a “high-throughput” have been made while utilizing the existing sensors and technology. However, the demand for ‘good’ phenotypic information for linkage to the genome in understanding the gene-environment interactions is still a bottleneck in the plant phenotyping technologies. Moreover, the available technologies and instruments are inaccessible, expensive and sometimes bulky.
This thesis work attempts to address some of the critical problems, such as exploration and development of a low-cost LiDAR-based platform for phenotyping the plants in-lab and in-field. A low-cost LiDAR-based system design, LiDARPheno, is introduced in this thesis work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction. A detailed design of the LiDARPheno, based on low-cost and off-the-shelf components and modules, is presented. Moreover, the design of the firmware to control the hardware setup of the system and the user-level python-based script for data acquisition is proposed. The software part of the system utilizes the publicly available libraries and Application Programming Interfaces (APIs), making it easy to implement the system by a non-technical user.
The LiDAR data analysis methods are presented, and algorithms for processing the data and extracting the leaf traits are developed. The processing includes conversion, cleaning/filtering, segmentation and trait extraction from the LiDAR data. Experiments on indoor plants and canola plants were performed for the development and validation of the methods for estimation of the leaf traits. The results of the LiDARPheno based trait extraction are compared with the SICK LMS400 (a commercial 2D LiDAR) to assess the performance of the developed system.
Experimental results show a fair agreement between the developed system and a commercial LiDAR system. Moreover, the results are compared with the acquired ground truth as well as the commercial LiDAR system. The LiDARPheno can provide access to the inexpensive LiDAR-based scanning and open the opportunities for future exploration
3D machine vision system for robotic weeding and plant phenotyping
The need for chemical free food is increasing and so is the demand for a larger supply to feed the growing global population. An autonomous weeding system should be capable of differentiating crop plants and weeds to avoid contaminating crops with herbicide or damaging them with mechanical tools. For the plant genetics industry, automated high-throughput phenotyping technology is critical to profiling seedlings at a large scale to facilitate genomic research. This research applied 2D and 3D imaging techniques to develop an innovative crop plant recognition system and a 3D holographic plant phenotyping system.
A 3D time-of-flight (ToF) camera was used to develop a crop plant recognition system for broccoli and soybean plants. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. Both 2D and 3D features were extracted and utilized for the plant recognition task. Broccoli and soybean recognition algorithms were developed based on the characteristics of the plants. At field experiments, detection rates of over 88.3% and 91.2% were achieved for broccoli and soybean plants, respectively. The detection algorithm also reached a speed over 30 frame per second (fps), making it applicable for robotic weeding operations.
Apart from applying 3D vision for plant recognition, a 3D reconstruction based phenotyping system was also developed for holographic 3D reconstruction and physical trait parameter estimation for corn plants. In this application, precise alignment of multiple 3D views is critical to the 3D reconstruction of a plant. Previously published research highlighted the need for high-throughput, high-accuracy, and low-cost 3D phenotyping systems capable of holographic plant reconstruction and plant morphology related trait characterization. This research contributed to the realization of such a system by integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard-pattern beacon array to track the 3D camera\u27s position and attitude, thus accomplishing precise 3D point cloud registration from multiple views. Specifically, algorithms of beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed. The phenotypic data obtained by this novel 3D reconstruction based phenotyping system were validated by the experimental data generated by the instrument and manual measurements, showing that the system has achieved measurement accuracy of more than 90% for most cases under an average of less than five seconds processing time per plant
Analysis of Argonaute-Small RNA-Transcription Factor Circuits Controlling Leaf Development
Experimental studies of plant development have yielded many insights into gene regulation, revealing interactions between core transcriptional and post-transcriptional regulatory pathways present in all land plants. This work describes a direct connection between the three main small RNA-transcription factor circuits controlling leaf shape dynamics in the reference plant Arabidopsis thaliana. We used a high-throughput yeast 1-hybrid platform to identify factors directly binding the promoter of the highly specialized ARGONAUTE7 silencing factor. Two groups of developmentally significant microRNA-targeted transcription factors were the clearest hits from these screens, but transgenic complementation analysis indicated that their binding sites make only a small contribution to ARGONAUTE7 function, possibly indicating a role in fine tuning. Timelapse imaging methodology developed to quantify these small differences may have broad utility for plant biologists. Our analysis also clarified requirements for polar transcription of ARGONAUTE7. This work has implications for our understanding of patterning in land plants
PlantCV v2: Image analysis software for high-throughput plant phenotyping
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning
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