Philippe Burger

An overview of the crop model stics

Abstract stics is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, stics relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of stics is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because stics is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the stics model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results

A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from ~1.5m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5° zenith angle perpendicularly to the row direction. The system is fixed to a horizontal beam supported by a tractor that moves along the micro-plots. About 100 micro-plots per hour were sampled by the system, the data being automatically collected and registered thanks to a centimetre precision geo-location. The green fraction (GF, the fraction of green area per unit ground area as seen from a given direction) was derived from the images with an automatic segmentation process and the reflectance spectra recorded by the radiometers were transformed into vegetation indices (VI) such as MCARI2 and MTCI. Results showed that MCARI2 is a good proxy of the GF, the MTCI as observed from 57° inclination is expected to be mainly sensitive to leaf chlorophyll pigments. The frequent measurements achieved allowed a good description of the dynamics of each micro-plot along the growth cycle. It is characterised by two periods: the first period corresponding to the vegetative stages exhibits a small rate of change of VI with time; followed by the senescence period characterised by a high rate of change. The dynamics were simply described by a bilinear model with its parameters providing high throughput metrics (HTM). A variance analysis achieved over these HTMs showed that several HTMs were highly heritable, particularly those corresponding to MCARI2 as observed from nadir, and those corresponding to the first period. Potentials of such semi-automatic measurement system are discussed for in field phenotyping applications.

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds⋅m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages.

A method to estimate plant density and plant spacing heterogeneity: application to wheat crops

BackgroundPlant 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.ConclusionsThis method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row.

Modeling the spatial distribution of plants on the row for wheat crops

A pipeline was developed to measure at the emergence stage the coordinates of plants on the row from RGB imagery.Plant spacing along the row is independent and follows a gamma distribution.The deviation of plants from the row direction follows a Gaussian distribution with a strong dependency on the position along the row.Impacts of the sowing pattern on the canopy structure were assessed using the 3D Adel-Wheat model. 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.

Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model

BackgroundLeaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b (

Estimation of green area index and chlorophyll content based on 3D canopy architecture models and the combination of gap fraction and hyperspectral reflectance measurements. Application to high throughput wheat phenotyping.

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