scholarly journals Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits

2020 ◽  
Vol 12 (23) ◽  
pp. 3877
Author(s):  
Shlomi Aharon ◽  
Zvi Peleg ◽  
Eli Argaman ◽  
Roi Ben-David ◽  
Ran N. Lati
Keyword(s):  
High Throughput ◽  
Growth Parameters ◽  
Early Growth ◽  
Field Conditions ◽  
Plant Phenotyping ◽  
Weed Competition ◽  
Initial Growth ◽  
Modeling Approaches ◽  
Cereal Breeding ◽  
Early Vigor

Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.

scholarly journals Avaliação do crescimento inicial de Jatropha curcas L. sob diferentes doses de fósforo aplicados na base

2013 ◽  
Vol 4 (4) ◽  
pp. 378-389 ◽  
Author(s):  
Rejanne Lima Arruda ◽  
Poliana Alves de Queiroz ◽  
Neumárcio Vilanova da Costa ◽  
Althiéris de Souza Saraiva ◽  
Eduardo Andrea Lemus Erasmo
Keyword(s):  
Plant Height ◽  
Jatropha Curcas ◽  
Growth Parameters ◽  
Phosphate Fertilizer ◽  
Early Growth ◽  
Phosphorus Fertilization ◽  
Initial Growth ◽  
Jatropha Curcas L ◽  
The One ◽  
Different Doses

This study aimed to analyze the influence of different doses of phosphorus (P2O5) applied at the base on the initial growth of Jatropha curcas L. The experimental randomized blocks design was used with four replications. The treatments consisted of the following doses of phosphorus fertilization: 0, 50, 100, 150 and 200 g plant-1 of P2O5 at 36, 60, 71, 85, 106, 140, 177, 199, 235 and 263 days after seedlings transplanting. Growth parameters evaluated were as follows: plant height, stem diameter, number of primary and secondary branches, number of inflorescences, fruits number, leaf area and seed yield. The P2O5 levels influenced the early growth of the plants. For most of the evaluated characteristics (plant height, number of primary branches, inflorescences, number of fruits), a dose of 150 g plant-1 was the one that promoted greater increase to 140 DAT. Due to the high genetic variability among plants, and the fact that scientists in the field of genetic improvement are working for the development of cultivars with desirable agronomic characteristics, it is necessary to conduct further studies with phosphate fertilizer for the cultivation of Jatropha.

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions

2021 ◽  
Vol 13 (7) ◽  
pp. 1380
Author(s):  
Sébastien Dandrifosse ◽  
Alexis Carlier ◽  
Benjamin Dumont ◽  
Benoît Mercatoris
Keyword(s):  
Accurate Method ◽  
Field Conditions ◽  
Plant Phenotyping ◽  
Average Error ◽  
Multiple Sources ◽  
Registration Method ◽  
Pixel Intensity ◽  
Local Distortion ◽  
Close Range ◽  
Multimodal Images

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.

scholarly journals Viability of Biofertilizer Produced by an Indian Biodigester Prototype Applied to Sunflower Plants

2017 ◽  
Vol 9 (10) ◽  
pp. 253
Author(s):  
Luciano Fernandes Moura ◽  
Pedro Felipe Sousa Teixeira ◽  
Franklin Aragão Gondim ◽  
Francisco Holanda Nunes Junior ◽  
Rifandreo Monteiro Barbosa ◽  
...  
Keyword(s):  
Dry Matter ◽  
Energy Use ◽  
Polynomial Regression ◽  
Growth Parameters ◽  
Low Cost ◽  
Control Group ◽  
Initial Growth ◽  
Sunflower Crop ◽  
Number Of Leaves ◽  
Relative Chlorophyll Content

Biodigesters have been used to convert biomass into biogas and biofertilizers. This energy use has been important for the reduction of solid waste pollution in the environment. This work aims to analyse the viability of the use of pig biofertilizer produced by an Indian biodigester prototype, monitored by a data acquisition system. The biodigester used was an Indian prototype built on a low cost material that is easy to acquire (polyvinyl chloride-PVC). After the biofertilizer production, we tested its efficiency and viability under conditions of vegetation house in the cultivation of sunflower plants. The experimental design was completely randomized in a factorial arrangement with 4 concentrations of biofertilizer (0, 40, 80 and 120 kg N ha-1) × 4 harvest periods (14, 21, 25 and 29 days after sowing). We evaluated biometric and vigor parameters by measurements of stem diameter, height of the aerial part, number of leaves and production of fresh and dry matter of roots, aerial and total parts, as well as the relative chlorophyll content. We performed the experiment with five repetitions using two plants each and we submitted the data to analysis of variance (ANOVA) and polynomial regression using the statistical software Sisvar 5.4. The functional Indian biodigester prototype produced a biofertilizer of excellent quality and viability as a biofertilizer for the initial growth of sunflower plants. The biofertilizer served as a nutritional source in the sunflower crop, since it provided increases in all the growth parameters analyzed in relation to the control group (plants in the absence of biofertilizer), especially in the concentration of 120 kg N ha-1.

scholarly journals In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy

2017 ◽  
Vol 114 (13) ◽  
pp. 3393-3396 ◽  
Author(s):  
Narangerel Altangerel ◽  
Gombojav O. Ariunbold ◽  
Connor Gorman ◽  
Masfer H. Alkahtani ◽  
Eli J. Borrego ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Stress Response ◽  
High Throughput ◽  
Plant Stress ◽  
Spectroscopic Technique ◽  
Plant Phenotyping ◽  
Plant Stress Response ◽  
Concentration Changes ◽  
Concentration Levels

Development of a phenotyping platform capable of noninvasive biochemical sensing could offer researchers, breeders, and producers a tool for precise response detection. In particular, the ability to measure plant stress in vivo responses is becoming increasingly important. In this work, a Raman spectroscopic technique is developed for high-throughput stress phenotyping of plants. We show the early (within 48 h) in vivo detection of plant stress responses. Coleus (Plectranthus scutellarioides) plants were subjected to four common abiotic stress conditions individually: high soil salinity, drought, chilling exposure, and light saturation. Plants were examined poststress induction in vivo, and changes in the concentration levels of the reactive oxygen-scavenging pigments were observed by Raman microscopic and remote spectroscopic systems. The molecular concentration changes were further validated by commonly accepted chemical extraction (destructive) methods. Raman spectroscopy also allows simultaneous interrogation of various pigments in plants. For example, we found a unique negative correlation in concentration levels of anthocyanins and carotenoids, which clearly indicates that plant stress response is fine-tuned to protect against stress-induced damages. This precision spectroscopic technique holds promise for the future development of high-throughput screening for plant phenotyping and the quantification of biologically or commercially relevant molecules, such as antioxidants and pigments.

scholarly journals Combined Influence of Chitosan and Calcium Chloride on Fusarium Dry Rot Disease Under Field Conditions

2020 ◽  
Vol 14 (1) ◽  
pp. 339-344
Author(s):  
Sabah R. Mohammed ◽  
Ivan D. Eskov ◽  
Elsayed M. Zeitar
Keyword(s):  
Plant Height ◽  
Calcium Chloride ◽  
Tuber Yield ◽  
Foliar Application ◽  
Growth Parameters ◽  
Disease Incidence ◽  
Field Conditions ◽  
Dry Rot ◽  
Fusarium Dry Rot ◽  
Rot Disease

Background: Fusarium dry rot disease caused by Fusarium sambucinum Fuckel (F. sambucinum) can infect the potato tubers in the field and during storage. Yield losses by F. sambucinum reach 60%. Traditional methods to control Fusarium dry rot are fungicides application, which led to developing many isolates resistant to these fungicides. Objective: The aim of this study is to evaluate the effect of calcium chloride (CaCl2) and chitosan, alone or in combination, on plant development, tuber yield, and Fusarium dry rot disease incidence under field conditions. Methods: Soil inoculated with F. sambucinum before planting. We treated the seed tubers with CaCl2 (0.5 or 1%), chitosan 0.5%, or both. The foliage was sprayed twice with CaCl2 (0.5 or 1%), 0.1% chitosan, or both. During the vegetation period, growth parameters, such as germination (%), plant height (cm), and branches number per plant, were measured. At harvest, we calculated the total and the marketable number of tubers and tuber yield. In addition, during storage, we assessed the incidence of Fusarium dry rot disease on tubers. Results: Results revealed that combined pre-planting application with 1% CaCl2 and 0.5% chitosan with 2 hours intervals, then spraying foliar with 1% CaCl2 and 0.1% chitosan twice with ten days intervals starting at 40 days after planting resulted in: a) increasing the germination, enhancing the growth parameters such as plant height and branches number per plant; b) enhancing the marketable tuber yield by 75.2 and 97.6% in Sante and Kolobok varieties, respectively; c) reducing Fusarium dry rot disease incidence by 61.9-72.7%. Conclusion: The work highlighted that the combined pre-planting and foliar application of CaCl2 and chitosan might be recommended for potato producers to reduce the incidence of Fusarium dry rot disease and augment yields.

scholarly journals ROOT PHENOTYPING FROM X-RAY COMPUTED TOMOGRAPHY: SKELETON EXTRACTION

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 417-422
Author(s):  
M. Herrero-Huerta ◽  
V. Meline ◽  
A. S. Iyer-Pascuzzi ◽  
A. M. Souza ◽  
M. R. Tuinstra ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Root System ◽  
High Throughput ◽  
Skeletal Structure ◽  
Plant Phenotyping ◽  
Skeleton Extraction ◽  
X Ray ◽  
Digital Twin ◽  
Three Samples ◽  
Root Phenotyping

Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.

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