Simon Madec

Cirad - La recherche agronomique pour le développement | CIRAD · Unité Mixte de Recherche Territoires, Environnement, Télédétection et Information Spatiale (TETIS)

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art metho...

Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation, which is an inefficient and tedious process. Be...

The GEOGLAM Crop Monitor for Early Warning is based on the integration of the crop conditions assessments produced by regional systems. Discrepancies between these assessments can occur and are generally attributed to the interpretation of the vegetation and climate data. The premise of this paper is that other sources of discrepancy related to the...

Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two step...

Full article available : https://spj.sciencemag.org/journals/plantphenomics/2022/9803570/

Multispectral observations from unmanned aerial vehicles (UAVs) are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation status. However, the limited autonomy of UAVs makes the completion of flights difficult when sampling large areas. Increasing the...

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 agricultura...

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are re...

Several crops bear reproductive organs (RO) at the top of the canopy after the flowering stage, such as ears for wheat, tassels for maize, and heads for sunflowers. RO present specific architecture and optical properties as compared to leaves and stems, which may impact canopy reflectance. This study aims to understand and quantify the influence of...

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace traditional visual counting in fields with improved throughput, accuracy and access to plant localization. However, high-resolution (HR) images are re...

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the computer vision and agricultural sc...

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning...

Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed methods for wheat head detection from high-resolution RGB imagery. They are based on computer vision and machi...

Accurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and...

Background: Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., s p i k e n u m b e r m - 2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effec...

Genetic progress is one of the major leverage used to increase food production and satisfy the needs for the increasing human population under global change issues. Selecting or creating the optimal cultivar for a given location is quite challenging considering the very large spatial and temporal variability of the environmental conditions. Field p...

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 straigh...

Hardware Software Structural traits Leaf traits Dynamics of traits Conclusion High resolution RGB camera + 6000 x 4000 pixels + Pixel size: 3.93 µm + Focal length: 30-60 mm + Weight: 500 g with lens Footprint 4,2mm Footprint 8mm AIRPHEN multispectral camera + 1080 x 960 pixels + 6 configurable bands + Pixel size: 3.04 µm + Focal length: 4.2-8 mm +...

Wheat 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 Fa...

Phenotyping in breeding trials is the basis for the selection of new varieties of food, feed and fibre crops that support the continued growth of world population. In addition to economic yield, breeders measure many phenotypes (also called traits) associated with the adaptation of these crops, such as the time of flowering, the height of the crop...

Leaf rolling in maize crops is one of the main plant reactions to water stress that can be visually scored in the field. However, leaf-scoring techniques do not meet the high-throughput requirements needed by breeders for efficient phenotyping. Consequently, this study investigated the relationship between leaf-rolling scores and changes in canopy...

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmann...

Leaf rolling in maize crops is one of the main plant reactions to water stress that may be visually scored in the field. However, the leaf scoring did not reach the high-throughput desired by breeders for efficient phenotyping. This study investigates the relationship between leaf rolling score and the induced canopy structure changes that may be a...

Imagery from unmanned aerial vehicle (UAV) combined with some computer vision techniques offers an efficient and low-cost opportunity in order to estimate the 3D representation of the canopy. From a multiple overlapping RGB images a Dense Cloud (DC) can be generated and the plant height (PH) can be derived. These crop heights are a valuable informa...

The PHENOMOBILE -LV is a fully automated robot designed for high -precision, high -throughput field phenotyping. It is equipped with several sensors including RGB cameras, spectroradiometers working in the visible and near infrared, and LIDARs. All these measurements are performed from nadir and inclined directions to gather complementary informati...

Solar irradiance forecasting is a necessity to ensure a safe and massive injection of photovoltaic energy into the electrical networks. At an intraday time horizon (up to 6 hours ahead) numerical weather predictions are not able to represent the cloud cover evolution at the scales required for solar energy purposes. Using subsequent HRV images of...

Cloud occurrence is an issue for the optical link availability of space based applications. Optical communications at 1.55μm between satellite and Optical Ground Stations (OGS) provide tremendous bandwidth compared to conventional radiofrequencies link. Nevertheless, cloud blockages of the optical signal require a network of geographically spread O...