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Abstract

This work investigates the spatial distribution of wheat plants and its consequences on the canopy structure. A set of RGB images were taken from nadir on a total 14 plots showing a range of sowing densities, cultivars and environmental conditions. The coordinates of the plants were extracted from RGB images. Results show that the distance between-plants along the row follows a gamma distribution law, with no dependency between the distances. Conversely, the positions of the plants across rows follow a Gaussian distribution, with strongly interdependent. A statistical model was thus proposed to simulate the possible plant distribution pattern. Through coupling the statistical model with 3D Adel-Wheat model, the impact of the plant distribution pattern on canopy structure was evaluated using emerging properties such as the green fraction (GF) that drives the light interception efficiency. Simulations showed that the effects varied over different development stages but were generally small. For the intermediate development stages, large zenithal angles and directions parallel to the row, the deviations across the row of plant position increased the GF by more than 0.1. These results were obtained with a wheat functional-structural model that does not account for the capacity of plants to adapt to their local environment. Nevertheless, our work will extend the potential of functional-structural plant models to estimate the optimal distribution pattern for given conditions and subsequently guide the field management practices.

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... The processing of images here was automatic except the last step corresponding to the interactive visual extraction of the plants' coordinates in the image. However, recent work (Jin et al. 2016;Liu et al. 2016b) suggests that it will be possible to automatize this last step to get a fully high-throughput method. ...
... In our experimental situations the sowing was considered as nominal on most of the fields. However, it will be possible to automatically identify from the images the nominal row segments from the places characterized by missing plants or excessive concentration of plants (Liu et al. 2016b). ...
... The gamma-count model proved to be well suited to describe the plant spacing distribution along the row over our contrasted experimental situations. It can thus be used to describe the heterogeneity of plant spacing as suggested by (Liu et al. 2016a). This may be applied for detailed canopy architecture studies or to quantify the impact of the sowing pattern heterogeneity on inter-plant competition (Olsen et al. 2006b;Olsen and Weiner 2007). ...
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Thesis
Crop production has to increase faster to meet the global food demand in the near future. Phenotyping, i.e. the monitoring crop state variables and canopy functioning quantitatively, was recognized as the bottleneck to accelerate genetic progress to increase the yield. Field phenotyping is mandatory since it allows evaluating the genotypes under natural field conditions. The technological advances of sensors, communication and computing foster the development of high-throughput phenotyping systems during the last decade. However, only limited attentions was paid in the interpretation of phenotyping measurements, leading to an under-exploitation of the potentials of current systems. This thesis focuses on advancing the interpretation of field phenotyping measurements over wheat crops. It includes three complementary aspects that illustrate the potentials of advanced image processing, model inversion and data assimilation for the interpretation of phenotyping measurements to access new traits or improve the accuracy with which already accessible traits have been retrieved. Several platforms (phenotypette, phenomobile, UAV) and sensors (RGB high resolution cameras, LiDAR) were used along this study.Characterization of the sowing pattern and density. The precise plant positions along and across the row was described from high resolution RGB images. Statistical models for the spacing of plants along the row and distance to the row center were then proposed and calibrated. The influence of the sowing pattern on the green fraction that can be easily measured with phenotyping techniques was then evaluated. The statistical model used to describe the distribution of plant spacing along the row was exploited to investigate the optimal sampling siz and method for plant density estimation. Finally, a method was developed to automatically estimate the plant density from the high resolution RGB images. Results show a relatively high accuracy when the spatial resolution is high enough and when observations are made before plants have reached 3 leaves stages.ADEL-Wheat model assisted Estimation of GAI from LiDAR measurements. It is relatively easy to achieve accurate GAI estimate using passive observations at early stages. However, the performances degrade for high GAI conditions due to the saturation problem. The use of LiDAR with its capacity to bring information on the third dimension was investigated as a possible way to alleviate the saturation effect based on the regularities between top and deeper canopy layers as described by the ADEL_Wheat model. The LiDAR used is equipping the phenomobile phenotyping platform. Focus was put on the stage of maximum GAI development when saturation effects are the largest. Results show a significant improvement of performances when using LiDAR observations as compared to classical green fraction based estimation.Assimilation of green fractions temporal evolution into ADEL-Wheat model. Monitoring the dynamics of canopy architecture to get early vigor traits of the crop is highly desired by breeders. The feasibility and interest of a phenotyping data assimilation approach was evaluated based on in silico experiments using the ADEL_Wheat model simulations. The green fraction observed from several view directions and dates is the variable that is assimilated. A sensitivity analysis was conducted to evaluate the effect of the number and spacing of the observation dates as well as the number of view directions used. Results show that few parameters of the ADEL-Wheat model are actually accessible from this assimilation technique. Further, it allows also estimating with a good accuracy emerging canopy properties such as the GAI and the number of stems with more than 3 leaves. Based on these innovative results, conclusions are finally drawn on the limits of this study and on the future work to undertake for efficient field high-throughput phenotyping
... The optimal allocation of plant layout has been valued by agricultural producers and researchers [2] . The determination of the layout of traditional agricultural crops is mostly based on expert experience [3,4] or field trials [2][3][4][5][6][7][8][9][10][11] . ...
... The optimal allocation of plant layout has been valued by agricultural producers and researchers [2] . The determination of the layout of traditional agricultural crops is mostly based on expert experience [3,4] or field trials [2][3][4][5][6][7][8][9][10][11] . Although the empirical model of experts is simple and practical, it tends to target certain characteristics of an area such as climate, geography, soil type, and crop planted, and therefore has less general applicability. ...
... The plant density evaluated over row segments needs to account for the uncertainties in row spacing. The variability of the row spacing is of the order of 10 mm as reported by [20] which corresponds to CV = 6% using a typical row spacing of 175 mm. For the sake of simplicity, the variability of row spacing will be neglected since it is likely to be small. ...
... The gamma-count model proved to be well suited to describe the plant spacing distribution along the row over our contrasted experimental situations. It can thus be used to describe the heterogeneity of plant spacing as suggested by [20]. This may be applied for detailed canopy architecture studies or to quantify the impact of the sowing pattern heterogeneity on inter-plant competition [1,2]. ...
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Background Plant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision. ResultsThree experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured. Conclusions This method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row.
... It is then possible to access a few phenological events such as heading [110] or flowering [97], and to describe the dynamics of canopy structure as a proxy of functional traits. The use of simple models or more sophisticated ones [101,107,111,112] offers great potential for providing breeders with new insights into the functioning of the crop. This will be the focus of future investigations where crop functioning models are combined with high-throughput phenotyping observations to tune model parameters that describe the reaction of the crop to environmental factors. ...
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There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker–trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.
... The upper tiller to appear was T 5 in low density treatments D70 (Caphorn, Apache and Renan) and T 3 in D200 (Caphorn). Given the coordination of tiller emergence with Haun stage (Abichou et al., 2016a), the Haun stage at cessation of tillering was estimated as HS =6.9 for D70 and HS = 5.5 for D200. In experiments E1, destructive measurements were conducted to follow the evolution of the leaf curvature of upper leaves: leaf 7 to flag leaf. ...
Thesis
Les modèles structure 3D de plante vise à représenter explicitement la dynamique de développement au cours du temps de l'architecture des plantes, en se basant sur les données expérimentales. Couplés avec les modèles physiques, ces modèles peuvent être utilisés comme outil pour étudier comment la structure des plantes module divers processus comme l'émission, le dépôt, le transfert d'entités biotiques et abiotiques dans le couvert. Toutefois, ces modéles necessitent un effort expérimental important pour les paramétrer. D'autres part, la représentation géométrique des plantes dans les modèles existants se fonde sur des approches statistiques et exploitent des bases de données très limitées. En réalité les plantes modulent l'inclinaison et l'orientation des feuilles et des tiges en fonction de l'environnement et de l'âge des organes. En travaillant sur le blé comme plante modéle, l'objectif de ce travail est de: 1. Réviser la paramétrisation de l'architecture de la plante dans le modèle Adel blé et d'améliorer le compromis entre flexibilité et complexité du modéle tout en réduisant l'effort expérimentale necessaire pour la calibration. 2. Analyser expérimentalement la représentation géométrique des plantes et son évolution au cours du temps en exploittant différents cultivars.
... Early growth vigor after seedling establishment (Coblentz et al. 2018) and plant height (Aslam et al. 2017) at the start of stem elongation (i.e., GS39) have been traditionally used as secondary screening and characterization traits associated with phenology. Wheat phenology can be reasonably accurately defined as a function of imagery-based thermal time (Haghshenas and Emam 2017); however, due to mitigating factors (e.g., sowing date, drought; population density), this approach may not be reliable to assess spatiotemporal variability in the phenology of large numbers of germplasm accessions (Hufkens et al. 2019) unless precisely-measured covariates are taken into consideration to adjust for special variation (Liu et al. 2017). ...
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In-depth information on plant phenology of einkorn (Triticum monococcum L. subsp. monococcum) and emmer, (Triticum turgidum subsp. dicoccun (Schrank) Thell) germplasm is indispensable for better utilization of their available genetic resources as underutilized crops, especially under abiotic stress. Whereas; optimization of their phenology is one of the most effective strategies to achieve this goal as it is a key factor for crop adaptation under abiotic stress. The objectives of this study were to integrate quantitative phenotyping methods to describe and explain phenotypic and genotypic differences or similarities in phenological stages within vegetative and reproductive growth phases between einkorn and emmer germplasm; identify combinations of discriminating traits between einkorn and emmer germplasm at successive phenological growth stages; and estimate multivariate distances between einkorn and emmer germplasm based on their geographical sources and stage of genetic improvement. The evaluated germplasm represented a wide range of geographical origins in the Fertile Crescent of West Asia, East Africa, West and East Europe, and North America. The study developed a method for accurate estimation of synchrony and duration of phenological growth stages of the diverse germplasm; and presented a ‘sliding’ scale capable of discriminating between different ‘maturity classes’ of the species on the basis of five phenology indicators, vis: growing degree days in conjunction with plant height, normalized difference vegetative index, color space coordinates, and green gradient-based canopy segmentation. Reliable relationship was established between visual scoring and color measurements of plants based on digital images at different growth stages. This relationship may become useful for research and crop management in resource-limited areas. Accurate prediction of phenological growth stages of einkorn and emmer wheat is essential, not only for ideotype development through simulation and modeling of weather and management effects, but also to help establish a cottage industry to benefit small-scale farmers, and to improve and maintain high-quality end products from these underutilized wheat genetic resources.
... The spatial distribution of plants in the area interferes with the use of solar radiation, intra and interspecific competition, use of fertilizer, among others. In addition, the response to wheat yield as a function of plant population varies according to the environment and genotype used (BARBIERI et al., 2013;TAVARES et al., 2014;LIU et al., 2017). ...
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In wheat crops, it is often observed that the number of plants per area is lower than that of viable seeds used, which may be related to both environmental conditions and seed vigor. The aim of this study was to assess the effect of seed vigor level at different sowing densities on growth, development, and grain yield in wheat cultivars. The experiments were conducted in Londrina and Ponta Grossa, PR, Brazil, under a randomized block design in a 2 × 2 × 3 factorial scheme, with four replications. Two seed vigor levels (high and low), two sowing densities (200 and 400 viable seeds per m2), and three wheat cultivars (BRS Sabiá, BRS Gaivota, and BRS Gralha Azul) were assessed. In order to assess growth and development, plant samples were collected at the phenological stages of seedling growth, stem elongation, booting, and ear emergence. We assessed the emergence of seedlings, height and dry matter of plant shoot, and grain yield. The sowing density of 200 seeds per m2 led to a higher shoot dry matter production per plant at the stages booting and ear emergence. The cultivar BRS Sabiá presented the highest grain yield in Londrina, while BRS Sabiá and BRS Gralha Azul presented the highest grain yield in Ponta Grossa. High vigor seeds favor stand establishment, growth and development of plants at early phenological stages, and grain yield of wheat.
... More details on the ADEL-Wheat model can be found in Abichou et al. (2013) and Fournier et al. (2003). The implementation of the model was taking into account the spatial distribution of plant positions according to the distribution laws proposed by Liu et al. (2016) (Fig. 6). ...
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The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes. High-throughput phenotyping canopy light interception in the field, therefore, would be of high interests for breeders to increase the efficiency of crop improvement. In this research, the Digital Plant Phenotyping Platform(D3P) was used to conduct in-silico phenotyping experiment with LiDAR scans over a wheat field. In this experiment virtual 3D wheat canopies were generated over 100 wheat genotypes for 5 growth stages, representing wide range of canopy structural variation. Accordingly, the actual value of traits targeted were calculated including GAI (green area index), AIA (average inclination angle) and FIPARdif (the fraction of intercepted diffuse photosynthetically activate radiation). Then, virtual LiDAR scanning were accomplished over all the treatments and exported as 3D point cloud. Two types of features were extracted from point cloud, including height quantiles (H) and green fractions (GF). Finally, an artificial neural network was trained to predict the traits targeted from different combinations of LiDAR features. Results show that the prediction accuracy varies with the selection of input features, following the rank as GF + H > H > GF. Regarding the three traits, we achieved satisfactory accuracy for FIPARdif (R2=0.95) and GAI (R2=0.98) but not for AIA (R2=0.20). This highlights the importance of H feature with respect to the prediction accuracy. The results achieved here are based on in-silico experiments, further evaluation with field measurement would be necessary. Nontheless, as proof of concept, this work further demonstrates that D3P could greatly facilitate the algorithm development. Morever, it highlights the potential of LiDAR measurement in the high-throuhgput phenopyting of canopy light interpcetion and structural traits in the field.
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Thesis
The simulation of plant architecture has become a very active front of research because of its importance for understanding the functioning of plants and their interactions with the environment. When analysing observations of experimental treatments, it is of a great interest to be able to simulate the architecture of the crop with sufficient fidelity to represent the specific traits resulting from the experiment. In this context, the objective of the thesis project was to develop an operational model allowing to simulate the 4D architecture of a collection of individual plants for the whole crop cycle and in a way faithful to the observations. Our approach builds on the experimental characterization of a range of commercial cultivars cultivated in the Paris region. These data represent a wide range of climatic sequences, sowing dates, densities of seedlings and nitrogen fertilization. The data analysis allowed us to identify stable and robust functions that describe the dynamics of appearance and mortality and the final dimensions of the different components of the plant. Our work brings also novel information on the evolution of their geometry and spatial organisation over time. These functions were coded into a model that describes the dynamics of the architecture of a collection of plants from their emergence to their full maturity. Our reconstruction method allowed us to generate 4D reconstructions for a large part of our experimental treatments; it has also been used in several projects carried out in parallel with this work. Our model can also be used to explore potential architectures traits in order to propose new ideotypes. Finally, our modelling approach can be applied to other cereals: it provides a framework for comparing patterns of morphology and development between species and provides a tool to study, by simulation, the impact of the architectural traits of each species.
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Presentation
A parameterisation of wheat architecture was developed, having high flexibility to simulate contrasted genotypes and growth conditions with a reasonably low number of parameters. Field measurements at 4-5 dates allowed to simulate crops from emergence to maturity with a good agreement between simulated and measured ground cover and GAI. Dynamics of leaf angles were shown to impact strongly ground cover.
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Plants react to their environment and to management interventions by adjusting physiological functions and structure. Functional–structural plant models (FSPM), combine the representation of three-dimensional (3D) plant structure with selected physiological functions. An FSPM consists of an architectural part (plant structure) and a process part (plant functioning). The first deals with (i) the types of organs that are initiated and the way these are connected (topology), (ii) co-ordination in organ expansion dynamics, and (iii) geometrical variables (e.g. leaf angles, leaf curvature). The process part may include any physiological or physical process that affects plant growth and development (e.g. photosynthesis, carbon allocation). This paper addresses the following questions: (i) how are FSPM constructed, and (ii) for what purposes are they useful? Static, architectural models are distinguished from dynamic models. Static models are useful in order to study the significance of plant structure, such as light distribution in the canopy, gas exchange, remote sensing, pesticide spraying studies, and interactions between plants and biotic agents. Dynamic models serve quantitatively to integrate knowledge on plant functions and morphology as modulated by environment. Applications are in the domain of plant sciences, for example the study of plant plasticity as related to changes in the red:far red ratio of light in the canopy. With increasing availability of genetic information, FSPM will play a role in the assessment of the significance towards plant performance of variation in genetic traits across environments. In many crops, growers actively manipulate plant structure. FSPM is a promising tool to explore divergent management strategies.
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The phenotypes of grasses show differences depending on growth conditions and ontogenetic stage. Understanding these responses and finding suitable mathematical formalizations are an essential part of the development of plant and crop models. Usually, a marked change in architecture between juvenile and adult plants is observed, where dimension and shape of leaves are likely to change. In this paper, the plasticity of leaf shape is analysed according to growth conditions and ontogeny. Leaf shape of Triticum aestivum, Hordeum vulgare and Zea mays cultivars grown under varying conditions was measured using digital image processing. An empirical leaf shape model was fitted to measured shape data of single leaves. Obtained values of model parameters were used to analyse the patterns in leaf shape. The model was able to delineate leaf shape of all studied species. The model error was small. Differences in leaf shape between juvenile and adult leaves in T. aestivum and H. vulgare were observed. Varying growth conditions impacted leaf dimensions but did not impact leaf shape of the respective species. Leaf shape of the studied T. aestivum and H. vulgare cultivars was remarkably stable for a comparable ontogenetic stage (leaf rank), but differed between stages. Along with other aspects of grass architecture, leaf shape changed during the transition from juvenile to adult growth phase. Model-based analysis of leaf shape is a method to investigate these differences. Presented results can be integrated into architectural models of plant development to delineate leaf shape for different species, cultivars and environmental conditions. Free Access http://aob.oxfordjournals.org/content/107/5/865.full
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This paper presents an architectural model of wheat (Triticum aestivum), designed to explain effects of light conditions at the individual leaf level on tillering kinetics. Various model variables, including blade length and curvature, were parameterized for spring wheat, and compared with winter wheat and other Gramineae species. The architectural model enables simulation of plant properties at the level of individual organs. Parameterization was based on data derived from an outdoor experiment with spring wheat cv. Minaret. Final organ dimensions of tillers could be modelled using the concept of relative phytomer numbers. Various variables in spring wheat showed marked similarities to winter wheat and other species, suggesting possibilities for a general Gramineae architectural model. Our descriptive model is suitable for our objective: investigating light effects on tiller behaviour. However, we plan to replace the descriptive modelling solutions by physiological, mechanistic solutions, starting with the localized production and partitioning of assimilates as affected by abiotic growth factors.
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Plant population density (PPD) influences plant growth greatly. Functional-structural plant models such as GREENLAB can be used to simulate plant development and growth and PPD effects on plant functioning and architectural behaviour can be investigated. This study aims to evaluate the ability of GREENLAB to predict maize growth and development at different PPDs. Two field experiments were conducted on irrigated fields in the North China Plain with a block design of four replications. Each experiment included three PPDs: 2.8, 5.6 and 11.1 plants m(-2). Detailed observations were made on the dimensions and fresh biomass of above-ground plant organs for each phytomer throughout the seasons. Growth stage-specific target files (a description of plant organ weight and dimension according to plant topological structure) were established from the measured data required for GREENLAB parameterization. Parameter optimization was conducted using a generalized least square method for the entire growth cycles for all PPDs and years. Data from in situ plant digitization were used to establish geometrical symbol files for organs that were then applied to translate model output directly into 3-D representation for each time step of the model execution. The analysis indicated that the parameter values of organ sink variation function, and the values of most of the relative sink strength parameters varied little among years and PPDs, but the biomass production parameter, computed plant projection surface and internode relative sink strength varied with PPD. Simulations of maize plant growth based on the fitted parameters were reasonably good as indicated by the linearity and slopes similar to unity for the comparison of simulated and observed values. Based on the parameter values fitted from different PPDs, shoot (including vegetative and reproductive parts of the plant) and cob fresh biomass for other PPDs were simulated. Three-dimensional representation of individual plant and plant stand from the model output with two contrasting PPDs were presented with which the PPD effect on plant growth can be easily recognized. This study showed that GREENLAB model has the ability to capture plant plasticity induced by PPD. The relatively stable parameter values strengthened the hypothesis that one set of equations can govern dynamic organ growth. With further validation, this model can be used for agronomic applications such as yield optimization.
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Image processing algorithms for individual corn plant and plant stem center identification were developed. These algorithms were applied to mosaicked crop row image for automatically measuring corn plant spacing at early growth stages. These algorithms utilized multiple sources of information for corn plant detection and plant center location estimation including plant color, plant morphological features, and the crop row centerline. The algorithm was tested over two 41 m (134.5 ft) long corn rows using video acquired two times in both directions. The system had a mean plant misidentification ratio of 3.7%. When compared with manual plant spacing measurements, the system achieved an overall spacing error (RMSE) of 1.7 cm and an overall R 2 of 0.96 between manual plant spacing measurement and the system estimates. The developed image processing algorithms were effective in automated corn plant spacing measurement at early growth stages. Interplant spacing errors were mainly due to crop damage and sampling platform vibration that caused mosaicking errors. © 2008 American Society of Agricultural and Biological Engineers.
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In-field variations in corn plant spacing and population can lead to significant yield differences. To minimize these variations, seeds should be placed at a uniform spacing during planting. Since the ability to achieve this uniformity is directly related to planter performance, intensive field evaluations are vitally important prior to design of new planters and currently the designers have to rely on manually collected data that is very time consuming and subject to human errors. A machine vision-based emerged crop sensing system (ECSS) was developed to automate corn plant spacing measurement at early growth stages for planter design and testing engineers. This article documents the first part of the ECSS development, which was the real-time video frame mosaicking for crop row image reconstruction. Specifically, the mosaicking algorithm was based on a normalized correlation measure and was optimized to reduce the computational time and enhance the frame connection accuracy. This mosaicking algorithm was capable of reconstructing crop row images in real-time while the sampling platform was traveling at a velocity up to 1.21 m s -1 (2.73 mph). The mosaicking accuracy of the ECSS was evaluated over three 40 to 50 m long crop rows. The ECSS achieved a mean distance measurement error ratio of -0.11% with a standard deviation of 0.74%. © 2008 American Society of Agricultural and Biological Engineers.
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Within-row plant spacing plays an important role in uniform distribution of water and nutrients among plants which affects the final crop yield. While manual in-field measurements of within-row plant spacing is time and labour intensive, little work has been done on an alternative automated process. We have attempted to develop an automatic system making use of a state-of-the-art 3D vision sensor that accurately measures within-row maize plant spacing. Misidentification of plants caused by low hanging canopies and doubles were reduced by processing multiple consecutive images at a time and selecting the best inter-plant distance calculated. Based on several small scale experiments in real fields, our system has been proven to measure the within-row maize plant spacing with a mean and standard deviation error of 1.60 cm and 2.19 cm, and a root mean squared error of 2.54 cm, respectively.
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Weed dynamics models are needed to design innovative weed management strategies. Here, we developed a 3D individual-based model called FlorSys predicting growth and development of annual weeds and crops as a function of daily weather and cropping practices: (1) crop emergence is driven by temperature, and emerged plants are placed onto the 3D field map, depending on sowing pattern, density, and emergence rate; plants are described as cylinders with their leaf area distributed according to height; (2) weed emergence is predicted by an existing submodel, emerged weed seedlings are placed randomly; (3) plant phenology depends on temperature; (4) a previously developed submodel predicts available light in each voxel of the canopy; after emergence, plant growth is driven by temperature; when shaded, biomass accumulation results from the difference between photosynthesis and respiration; shading causes etiolation; (5) frost reduces biomass and destroys plants, (6) at plant maturity, the newly produced seeds are added to the soil seed bank. The model was used to test different sowing scenarios in an oilseed rape/winter wheat/winter barley rotation with sixteen weed annuals, showing that (1) crop yield loss was negatively correlated to weed biomass averaged over the cropping season; (2) weed biomass was decreased by scenarios allowing early and homogenous crop canopy closure (e.g. reduced interrows, increased sowing density, associated or undersown crops), increased summer fatal weed seed germination (e.g. delayed sowing) or, to a lesser degree, cleaner fields at cash crop sowing (e.g. sowing a temporary cover crop for “catching” nitrogen); (3) the scenario effect depended on weed species (e.g. climbing species were little affected by increased crop competition), and the result thus varied with the initial weed community (e.g. communities dominated by small weed species were hindered by the faster emergence of broadcast-sown crops whereas taller species profited by the more frequent gap canopies); (4) the effect on weed biomass of sowing scenarios applied to one year was still visible up to ten years later, and the beneficial effect during the test year could be followed by detrimental effects later (e.g. the changed tillage dates accompanying catch crops reduced weed emergence in the immediately following cash crop but increased seed survival and thus infestation of the subsequent crops). This simulation showed FlorSys to predict realistic potential crop yields, and the simulated impact of crop scenarios was consistent with literature reports.
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Individual variability generally exists in crop fields. It increases with an increase in plant population density, water or nutrient deficiency, or spatio-temporal irregularity, and often results in a reduction in yield. As individual variability exists in a community but is expressed through individuals, we studied it by applying two models, one at the stand level and the other at the individual level. The crop model PILOTE and the functional structural plant model (FSPM) GreenLab were applied to a field of maize (Zea mays L.) to provide a numerical description of the crop at different levels. The delay and slower increase in LAI and in total dry matter at stand level compared to individual level, led us to hypothesize that uneven emergence could have an effect on variability. We derived a theoretical distribution of germination dates, which supported this hypothesis. In parallel, we used GreenLab to analyze possible sources of variability in accumulated biomass within a dynamic system, and to estimate possible parameters from experimental data. Using PILOTE and GreenLab, we successfully identified two typical types of individual variability in the maize field: variability in development over time and variability in competition for space during growth. Our method could be used in future research on the cause and influence of individual variability on performance, and to identify the link between an FSPM based on individual plants and a crop model at stand level.
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An inter-plant spacing sensing system using a TOF (time of flight) of light based 3D sensor was developed. The 3D sensor was capable of capturing distance information, intensity and amplitude data in a single shot. The side view depth images captured were stitched together using distance information from a wheel encoder in conjunction with a feature-based image sequencing process for the stem location identification. One obvious advantage of the system over current color-based 2D systems was the use of depth images for plant identification, which was less sensitive to color variations. A covered cart was designed to prevent the sunlight from directly shedding on the plants and to reduce the interference from wind, which in turn made the system usable throughout the day. The vertical camera position was easily adjustable making the system suitable to work with plants at different growth stages.The use of side-view images made the system capable of detecting inclined plants and therefore, boosted the performance of the system in precisely locating the stem centers, which in turn minimized the measurement errors. The measurement accuracy demonstrated the system superiority over the current systems which make use of top-view images for inter-plant spacing sensing. The system achieved an overall mean root mean squared error (RMSE) of 0.017 m with a mean plant misidentification ratio of 2.2%. The coefficient of determination (R2) was 0.95 between the in-field manual distance measurements and the system distance estimates.
Article
Summary It has been hypothesized that increased crop density and spatial uniformity can increase weed suppression and thereby play a role in weed management. Field experi- ments were performed over 2 years to investigate the effects of the density and spatial arrangement of spring wheat (Triticum aestivum) on weed biomass and wheat yield in weed-infested fields. We used three crop spatial patterns (normal rows, random and uniform) and three densities (204, 449 and 721 seeds m)2), plus a fourth density (1000 seeds m)2) in the random pattern. Increased crop density reduced weed biomass in all three patterns. Weed biomass was lower and crop biomass higher in wheat sown in the random and uniform patterns than in normal rows in both years. At 449 seeds m)2, weed biomass was 38% lower in the uniform and 27% lower in the random pattern than in rows. There was evidence of decreasing grain yield due to intraspecific competition only at 1000 seeds m)2. The results not only confirm that increasing density and increasing crop spatial uniformity increase the suppres- sion of weeds, but also suggest that a very high degree of spatial uniformity may not be necessary to achieve a major increase in weed suppression by cereal crops. Rows represent a very high degree of spatial aggre- gation. Decreasing this aggregation increased weed suppression almost as much as sowing the crop in a highly uniform spatial pattern. While the random pattern produced as much crop biomass and suppressed weeds almost as well as the uniform pattern, the uniform pattern gave the highest yield.
Article
Equations are derived relating errors in seed release from seed delivery mechanisms to seed spacing distributions in the furrow. Factors taken into account in these equations include the height of release, the radius of cell wheel mechanisms or the angle of the belt in belt type mechanisms, and the relative velocities of the seed at release point.A model is described which predicts the optimum release point for a cell wheel mechanism and experimental results are described which support this prediction. The factor with the most outstanding effect is the ratio between the forward speed of the drill and the speed of the release points on the metering mechanism; when the ratio is unity release errors have a minimal effect upon spacing distribution.
Article
Maize canopies with a synchronous seedling emergence and a uniform plant spatial distribution exhibit early-established plant hierarchies (at the 4-leaf stage; V4). The dominant and dominated individuals of the stand differ in plant growth rate during both the pre-silking period (i.e. from V7 to V13; PGRPS) and the period around silking (i.e. a 30 d period centered in silking; PGRS), and in the ear growth rate around silking (EGRS). Based on the depleted availability of assimilates of the dominated plants, we tested the hypotheses that (i) the low PGRPS of dominated individuals affects the morphogenesis of the apical ear leading to a low number of completely developed flowers per ear, and (ii) the low EGRS of dominated individuals results in a pronounced asynchrony of flowering dynamics and uneven silk exsertion from the husks. Two hybrids with contrasting tolerance to crowding stress (DK752 and DK765 as the tolerant and the intolerant hybrid, respectively) were cropped under different intensities of interplant competition (6, 12, 12plantsm−2 thinned to 6plantsm−2 at V9 and 6plantsm−2 shaded from V9 onwards) during 2004/2005 and at 12plantsm−2 during 2005/2006 at Pergamino (34°56′S 60°34′W), Argentina. Dominant plants were the individuals of the stands with the highest PGRPS (ca. 1.72 and 2.56gd−1 for dominated and dominant plants, respectively), PGRS (ca. 3.05 and 3.94gd−1 for dominated and dominant plants, respectively) and EGRS (ca. 1.06 and 1.55gd−1 for dominated and dominant plants, respectively). This plant type also exhibited the most synchronous flowering dynamics (anthesis–silking interval ca. 1.49 and 1.15 days for dominated and dominant plants, respectively) and the highest kernel set (ca. 401 and 572kernelsplant−1 for dominated and dominant plants, respectively). Apical ears of dominated plants exhibited a delayed in the rate of progress to successive floral stages, but the final number of completely developed flowers per ear did not differ between extreme plant types (ca. 967 and 803 completely developed flowers per ear for DK752 and DK765, respectively). Hence, kernel number per plant was not limited by the number of completely developed flowers per ear, but flowering dynamics were a decisive factor in kernel set of both plant types. Asynchronous silking within the ear of dominated plants determined a greater proportion of flowers per ear with non-exposed silks on silking+5 d and a larger asynchrony in silk extrusion within the ear. These responses increased kernel abortion rate respect to figures obtained for dominant individuals.
Article
Nonuniform plant spacing within the row in corn (Zea mays L.) may reduce grain yield. To investigate the response of corn to plant spacing variability, experiments were conducted at two locations in south-central Ontario during 2000 and 2001. Six plant spacing treatments, 6.7 to 16.2 cm in standard deviations (SD), were established by planting Roundup Ready corn with increasing proportions of conventional corn seeds and then removing the conventional corn using glyphosate before three-leaf stage. Using SD as well as short gap, long gap, double, and cluster as an index of plant spacing variability, effects of plant spacing variability on corn growth and grain yield were investigated. Averaged across locations and years, grain yield was not significantly affected by plant spacing variability. Plant spacing variability also had no significant effect on leaf number, plant height, leaf area index, and harvest index. There were no correlations between plant spacing variability and stalk lodging and barren or double ears. The lack of strong correlations among plant growth, grain yield, and plant spacing variability indicates that spacing uniformity within the range used in this study is not a significant factor in determining grain yield under commercial conditions and common plant densities used in Ontario.
Article
A method based on the prediction of order statistics is proposed to select the underlying parent distribution. A cross-validatory predictor and the best linear unbiased predictor are considered in choosing between gamma and Weibull models when shape parameters are only known to lie within a range. The proposed approach is evaluated using a large-scale Monte Carlo study. The results clearly show that the cross-validatory predictor performs well as a robust procedure in selecting between probability densities. Two well-known data sets are used to illustrate the procedure.
Article
Due to the individual volumes of fluted wheel metering systems each holding more than one seed, seed drills provide random seed distribution. A prerequisite for the improvement of seed spacing is the fast and reliable evaluation of distribution accuracy in laboratory tests. A high-speed camera system for evaluating seed spacing uniformity and velocity of fall of seeds is described. The performance of the high-speed camera system in terms of seed spacing evaluation was compared with a sticky belt test stand, used as a reference. Identical seed patterns were evaluated applying both methods simultaneously using wheat and soybean seeds. The speed of the metering rollers of the seed drill was set at 10, 20, 30 and 40 rpm and that of the seed drill at a simulated travelling speed of 1 m/s. In general, the high-speed camera system worked well in obtaining the seed spacing and velocity of fall of seeds. In all the tests with the wheat and soybean seeds, the high-speed camera system did not miss any seed. The sowing uniformity of the seed drill as investigated was affected by the speed of the metering rollers. Coefficient of variation of seed spacing, velocity of fall and coefficient of variation of velocity of fall of seeds decreased as the speed of the metering rollers increased.
Article
Summary 1. Recent advances in our understanding of the advantage of initial size in competition among individual plants (size-asymmetric competition) suggest that the potential for many crops to suppress weeds is much greater than generally appreciated. We hypoth- esize that this potential can be realized if: (i) the crop density is increased significantly and (ii) the crop is regularly (uniformly) distributed in two-dimensional space rather than sown in traditional rows. 2. We tested these hypotheses by sowing four varieties of spring wheat Triticum aestivum at three densities (200, 400 and 600 m -2 ) and in two spatial patterns (normal rows and a uniform grid pattern) in the presence of high weed pressure. 3. There were strong and significant effects of both crop density and spatial distribution on weed growth. Weed biomass decreased with crop density and was 30% lower in the grid pattern. 4. There was a negative linear relationship between above-ground weed biomass in early July and crop yield at harvest, so weed suppression translated directly into yield. The treatment with high crop density and the grid sowing pattern contained 60% less weed biomass and produced 60% higher yield than the treatment closest to normal sowing practices (crops sown in rows at 400 m -2 ).
Article
Summary • The effect of temperature on the minimum (base) water potential for seed germination (Ψb) was investigated in Daucus carota and Allium cepa and then described in two hydrothermal threshold models. • Germination was recorded over a wide range of temperatures and water potentials. • At temperatures of 15°C and below the base water potential for germination of the 50th percentile (Ψb(50)) was constant, but in both species, above a temperature (Td) around 16–19 °C, Ψb(50) increased linearly with temperature. Hydrothermal time (HTT) and virtual osmotic potential (VOP) models were altered so that the effective base water potential (Ψb(G,T)) for any percentile of the seed population (G), above Td, was given by Ψb(G)d + m(T – Td), where Ψb(G)d is the uncorrected base water potential for that percentile. The coefficient m is the slope of the linear relationship between Ψb(50) and temperature above Td. • Germination response to all temperatures and water potentials can be adequately described in both the HTT and VOP models by incorporating changes in Ψb(G,T) with temperature.
Article
To better understand the potential for improving weed management in cereal crops with increased crop density and spatial uniformity, we conducted field experiments over two years with spring wheat (Triticum aestivum) and four weed species: lambsquarters (Chenopodium album) , Italian ryegrass (Lolium multiflorum), white mustard (Sinapis alba), and chickweed (Stellaria media). The crops were sown at three densities (204, 449, and 721 seeds m−2) and in two spatial patterns (normal rows and a highly uniform pattern), and the weeds were sown in a random pattern at a high density. In most cases, the sown weeds dominated the weed community but, in other cases, naturally occurring weeds were also important. There were strong and significant effects regarding the weed species sown, the crop density, and the spatial distribution on the weed biomass in both years. The weed biomass decreased with increased crop density in 29 out of 30 cases. On average, the weed biomass was lower and the grain yield was higher in the uniform compared to the row pattern in both 2001 and 2002. Despite the differences in weed biomass, the responses of L. multiflorum, S. media, and C. album populations to crop density and spatial uniformity were very similar, as were their effects on the grain yield. Sinapis alba was by far the strongest competitor and it responded somewhat differently. Our results suggest that a combination of increased crop density and a more uniform spatial pattern can contribute to a reduction in weed biomass and yield loss, but the effects are smaller if the weeds are taller than the crop when crop–weed competition becomes intense.
Article
This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.
Article
Competition is a key process in plant populations and communities. We thus need, if we are to predict the responses of ecological systems to environmental change, a comprehensive and mechanistic understanding of plant competition. Considering competition, however, only at the population level is not sufficient because plant individuals usually are different, interact locally, and can adapt their behaviour to the current state of themselves and of their biotic and abiotic environment. Therefore, simulation models that are individual-based and spatially explicit are increasingly used for studying competition in plant systems. Many different individual-based modelling approaches exist to represent competition, but it is not clear how good they are in reflecting essential aspects of plant competition. We therefore first summarize current concepts and theories addressing plant competition. Then, we review individual-based approaches for modelling competition among plants. We distinguish between approaches that are used for more than 10 years and more recent ones. We identify three major gaps that need to be addressed more in the future: the effects of plants on their local environment, adaptive behaviour, and below-ground competition. To fill these gaps, the representation of plants and their interactions have to be more mechanistic than most existing approaches. Developing such new approaches is a challenge because they are likely to be more complex and to require more detailed knowledge and data on individual-level processes underlying competition. We thus need a more integrated research strategy for the future, where empirical and theoretical ecologists as well as computer scientists work together on formulating, implementing, parameterization, testing, comparing, and selecting the new approaches.
Article
The influence of maize architectural characteristics on gap fraction (Po) is investigated based on a 4D canopy model. This model simulates maize canopies from few input variables: maximum leaf area per plant, maximum number of leaves, plant density, distance between rows, leaf orientation plasticity, maximum height of plant and plant growing stage. A large number of scenes were constructed and the corresponding gap fraction was computed to perform a sensitivity analysis. Results show that leaf azimuth orientation that drives leaf overlapping and thus creates clumping appears to be a key variable. The effect is maximum for near nadir directions, where gap fraction often tends towards a significant non-zero limit, Pomin, when leaf area index (LAI) is very high. In these conditions, Poisson and extended Poisson models relating Po to LAI are not valid when considering the variation of LAI across development stages. A simple parametric model is proposed Po(θ)=Pomin(θ)+(1−Pomin(θ)) exp−K(θ) LAI, the two parameters (Pomin(θ) and K(θ)) depending on the architectural characteristics. This model describes with great accuracy the gap fraction across development stages (RMSE = 0.0135). The sensitivity of parameters Pomin and K to canopy architecture variables was analyzed for the nadir direction, confirming the previous findings. The ensemble of simulations generated for the sensitivity analysis was finally used as a look-up-table (LUT), to estimate Pomin and K values from the 4D model input variables. This allows to simulate the gap fraction for a large range of maize canopy architecture with high accuracy (RMSE = 0.0140). Application of this approach is discussed with due attention to light interception by the plants and the monitoring of canopies from remote sensing observations.
Article
Recently the two-parameter generalized exponential (GE) distribution was introduced by the authors. It is observed that a GE distribution can be considered for situations where a skewed distribution for a non-negative random variable is needed. The ratio of the maximized likelihoods (RML) is used in discriminating between Weibull and GE distributions. Asymptotic distributions of the logarithm of the RML under null hypotheses are obtained and they are used to determine the minimum sample size required in discriminating between two overlapping families of distributions for a user specified probability of correct selection and tolerance limit.
Article
Though some two-dimensional (2D) machine vision–based systems for early-growth-stage corn plant sensing exist, some of their shortcomings are difficult to overcome. The greatest challenge comes from separating individual corn plants with overlapped plant canopies. With 2D machine vision, variation in outdoor lighting conditions and weeds in the background also pose difficulties in corn plant identification. Adding the depth dimension has the potential to improve the performance of such a sensing system. A new corn plant sensing system using a real-time stereo vision system was investigated in this research. Top-view depth images of corn plant canopy were acquired. By processing the depth images, the algorithm effectively updated the plant skeleton structures and finally recognized individual corn plants and detected their center positions. The stereo vision system was tested over corn plants of V2–V3 growth stages in both laboratory and field conditions. Experimental results showed that the stereo vision system was capable of detecting both separated and overlapped corn plants. During the field test, 96.7% of the corn plants were correctly detected, and plant center positions were estimated with maximum distance errors of 5 and 1 cm for 74.6% and 62.3% of detections, respectively. © 2009 Wiley Periodicals, Inc.
Article
Summary To date, several crop : weed competition models have been developed. Developers of the various models were invited to compare model performance using a common data set. The data set consisted of wheat and Lolium rigidum grown in monoculture and mixtures under dryland and irrigated conditions. Results from four crop : weed competition models are presented: almanac, apsim, cropsim and intercom. For all models, deviations between observed and predicted values for monoculture wheat were only slightly lower than for wheat grown in competition with L. rigidum, even though the workshop participants had access to monoculture data while parameterizing models. Much of the error in simulating competition outcome was associated with difficulties in accurately simulating growth of individual species. Relatively simple competition algorithms were capable of accounting for the majority of the competition response. Increasing model complexity did not appear to dramatically improve model accuracy. Comparison of specific competition processes, such as radiation interception, was very difficult since the effects of these processes within each model could not be isolated. Algorithms for competition processes need to be modularised in such a way that exchange, evaluation and comparison across models is facilitated.
Article
This review introduces the emergence of a new research topic, phylloclimate, located at the crossroads between ecophysiology and canopy microclimate research. Phylloclimate corresponds to the physical environment actually perceived by each individual aerial organ of a plant population, and is described by physical variables such as spectral irradiance, temperature, on-leaf water and features of around-organ air (wind speed, temperature, humidity, etc.). Knowing the actual climate in which plant organs grow may enable advances in the understanding of plant-environment interactions, as knowing surface temperature instead of air temperature enabled advances in the study of canopy development. Characterizing phylloclimate variables, using experimental work or modeling, raises many questions such as the choice of suitable space- and time-scale as well as the ability to individualize plant organs within a canopy. This is of particular importance when aiming to link phylloclimate and function-structure plant models. Finally, recent trends and challenging questions in phylloclimate research are discussed, as well as the possible applications of phylloclimate results.
Effect of planting equipment and techniques on seed germination and emergence: a review
  • Orzolek
Orzolek, M.D., Daum, D.R., 1984. Effect of planting equipment and techniques on seed germination and emergence: a review. J. Seed Technol. 9, 99-113.
Increased density and spatial uniformity increase weed suppression by spring wheat
  • Olsen
Measurements for functional-structural plant models
  • Van der Heijden