Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version

Geometric models for plant leaf area estimation from 3D point clouds: a comparative study

International audienceMeasuring leaf area is a critical task in plant biology. Meshing techniques, parametric surface modelling and implicit surface modelling allow estimating plant leaf area from acquired 3D point clouds. However, there is currently no consensus on the best approach because of little comparative evaluation. In this paper, we provide evidence about the performance of each approach, through a comparative study of four meshing, three parametric modelling and one implicit modelling methods. All selected methods are freely available and easy to use. We have also performed a parameter sensitivity analysis for each method in order to optimise its results and fully automate its use. We identified nine criteria affecting the robustness of the studied methods. These criteria are related to either the leaf shape (length/width ratio, curviness, concavity) or the acquisition process (e.g. sampling density, noise, misalignment, holes). We used synthetic data to quantitatively evaluate the robustness of the selected approaches with respect to each criterion. In addition we evaluated the results of these approaches on five tree and crop datasets acquired with laser scanners or photogrammetry. This study allows us to highlight the benefits and drawbacks of each method and evaluate its appropriateness in a given scenario. Our main conclusion is that fitting a Bézier surface is the most robust and accurate approach to estimate plant leaf area in most cases

GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: theoretical considerations based on 3D architecture models and application to wheat crops

International audienceThis study describes a technique to estimate green area index (GAI) of row crops from gap fraction measurements at 57.5° perpendicular to the row using downward looking digital photos. This particular directional configuration makes the gap fraction independent from leaf angle distribution and minimizes leaf clumping when plants overlap within the row and when rows overlap from this particular direction which is the case for several crops including wheat, maize, sorghum, sunflower and soybean. This was demonstrated from generic row crop canopy architecture models. Additional simulations over realistic 3D scenes of wheat crop allowed to calibrate the following equation relating the gap fraction (Po(57.5°)) to GAI for this particular directional configuration: Po(57.5°) = e−0.824·GAI. This relationship appears very robust across development stages, cultivars and variations due to environmental conditions. When comparing with the situation where leaves are randomly distributed, performances degrade significantly, demonstrating that some residual clumping (Ω = 0.89) has to be accounted for. Field experiments were conducted over wheat crops using colour digital photos taken at 57.5° zenith angle from above in a compass direction perpendicular to the rows. The corresponding gap fraction was computed after image segmentation based on the three colours. The equation derived from wheat architecture model simulations was then used to estimate GAI. Comparison with destructive GAI field measurements shows very good performances with a relative RMSE of 12%. GAI values estimated with this technique were also showing a good consistency with LAI2000 PAI (plant area index) estimates. However, systematic biases between the two estimates were observed, due to canopy elements at the bottom of the canopy not sampled by the instrument because of the height of the LAI2000 sensor, as well as accounting for the residual clumping in the proposed method. These results suggest that this GAI estimation method is very efficient over wheat crops from emergence up to flowering independently from possible architecture variation due to genotype or environmental condition differences. Possible extension to other crops is discusse

High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass

Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest. It provided a ground spatial resolution better than 0.2 mm. A Faster Regional Convolutional Neural Network (Faster-RCNN) deep-learning model was first trained to identify the stems cross section. Results showed that the identification provided precision and recall close to 95%. Further, the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7% and robustness across the two experimental sites. The estimated stem density was also compared with the ear density measured in the field with traditional methods. A very high correlation was found with almost no bias, indicating that the stem density could be a good proxy of the ear density. The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher (80%) than that of the ear density (78%). The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section. Results show that the stem diameters follow a gamma distribution over each microplot with an average diameter close to 2.0 mm. Finally, the biovolume computed as the product of the average stem diameter, the stem density, and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%. Possible limitations of the findings and future applications are finally discussed

Caractérisation de la plasticité du maïs par phénotypage 3D en plein champ

International audienc

ACT: A leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface

International audienceLeaf reflectance of monocotyledons generally displays a strong azimuthal anisotropy due to the longitudinal orientation of the veins. The Cook and Torrance (CT) bidirectional reflectance distribution function model was adapted to account for this distinctive feature. The resulting ACT (Anisotropic Cook and Torrance) model is based on the decomposition of the roughness parameter into two perpendicular components. It is evaluated on sorghum (Sorghum halepense) and wheat (Triticum durum) leaf BRF (Bidirectional Reflectance Factor) measurements acquired using a conoscope system. Results show that the ACT model fits the measurements better than azimuthally isotropic surface models: the root mean square error computed over all the BRF measurements for both leaves decreases from ≈0.06 for the Lambertian model to ≈0.04 for the CT model and down to ≈0.03 for the ACT model. The adjusted value of the refraction index is plausible (n ≈ 1.32) for both leaves while the retrieved roughness values perpendicular to the veins (sorghum = 0.56; wheat = 0.46) is about two times larger than that parallel to the veins (sorghum = 0.27; wheat = 0.18). Nonetheless, the observed residual discrepancies between the ACT model simulations and the measurements may be explained mainly by the Lambertian assumption of the volume scattering

Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images

The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m−2) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R2 = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m−2, 0.4 m row spacing) towards 8 (12 plants.m−2, 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m−2, 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low

Estimating canopy characteristics from ground-based LiDAR measurement assisted with 3D Adel-Wheat model

poster abstractPhenotyping is rapidly evolving from a small set of destructive measurement and visual notations of few simple traits to massive characterizations derived from high-throughput non-invasive proximal or remote sensing techniques. Meanwhile functional structural plant modeling (FSPM) integrates the physiological and morphological information from organizational scales to the canopy level. The combination of new phenotyping techniques with FSPMs is expected therefore to estimate a set of FSPMs parameters corresponding to traits of interest. LiDAR (Light Detection And Ranging) is recently exploited for detailed 3D description of the canopy structure, especially over dense canopies with small elements such as wheat and barley. In this work, we propose to use a model-assisted phenotyping approach to improve our understanding of the interaction between laser beam and canopy. It leads us to develop inversion algorithm to retrieve canopy traits. A discrete LiDAR scanning simulator was first developed based on PlantGL, a 3D plant modeling python library. The footprint and the geometrical configuration of the LiDAR are explicitly accounted for. The LiDAR simulator was validated over an artificial crop made of 45 artificial plants. Actual LiDAR measurements were performed over the same scene. Results proved that the simulator generates a 3D point cloud having the same statistical properties as those derived from the actual LiDAR measurements. Then a synthetic experiment was completed to demonstrate the potentials of model assisted phenotyping. 3D wheat canopy scenes were generated with AdelWheat model for two contrasting development stages corresponding to thermal time 500 °C•day and 1500 °C•day across a wide range of combination of the model parameters (242 cases replicated 20 times). The scenes were transformed into 3D point clouds using the LiDAR simulator. A set of 50 independent cases were generated in addition to evaluate the performances of the method. Results demonstrate that emerging properties could be retrieved with a good accuracy from the 3D map including the leaf area index (LAI) (R2 = 0.85 and rRMSE = 6.11%). The retrieval of other parameters will be discussed with due attention to the complexity of the comparison of simulated 3D point clouds with the measured ones

Ear density estimation from high resolution RGB imagery using deep learning technique

International audienceWheat ear density estimation is an appealing trait for plant breeders. Current manual counting is tedious and inefficient. In this study we investigated the potential of convolutional neural networks (CNNs) to provide accurate ear density using nadir high spatial resolution RGB images. Two different approaches were investigated, either using the Faster-RCNN state-of-the-art object detector or with the TasselNet local count regression network. Both approaches performed very well (rRMSE approximate to 6%) when applied over the same conditions as those prevailing for the calibration of the models. However, Faster-RCNN was more robust when applied to a dataset acquired at a later stage with ears and background showing a different aspect because of the higher maturity of the plants. Optimal spatial resolution for Faster-RCNN was around 0.3 mm allowing to acquire RGB images from a UAV platform for high-throughput phenotyping of large experiments. Comparison of the estimated ear density with in-situ manual counting shows reasonable agreement considering the relatively small sampling area used for both methods. Faster-RCNN and in-situ counting had high and similar heritability (H-2 approximate to 85%), demonstrating that ear density derived from high resolution RGB imagery could replace the traditional counting method

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions