High resolution thermal and multispectral UAV imagery for precision assessment of apple tree response to water stress

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières(Edited by Pablo Gonzalez-de-Santos and Angela Ribeiro)This manuscript presents a comprehensive methodology to obtain Thermal, Visible and Near Infrared ortho-mosaics, as a previous step for the further image-based assessment of response to water stress of an experimental apple tree orchard. Using this methodology, multi-temporal ortho-mosaics of the field plot were created and accuracy of ortho-rectification and geo-location computed. Unmanned aerial vehicle (UAV) flights were performed on an irrigated apple tree orchard located in Southern France. The 6400 m² plot was composed of 520 apple trees which were disposed in 10 rows. In this field set-up, five well irrigated rows alternated with five rows submitted to progressive summer water constraints. For remote image acquisition, on 4th July, 19th July, 1st August and 6th September UAV flights with three cameras onboard (thermal, visible and near infrared) were performed at solar noon. On 1st August, five successive UAV flights were carried out at 8, 10, 12, 14 and 16 h (solar time). By using selfdeveloped software, frames were automatically extracted from the recorded thermal video and turned in the right image format. The temperature of four different targets (hot, cold, wet and dry bare soil) was continuously measured by the IR120 thermoradiometers during each flight, for radiometric calibration purpose. Based each on thirty images, all ortho-mosaics were successfully obtained. As high spatial resolution imagery requires high precision geo-location, and the root mean squared error (RMSE) of each ortho-mosaic positioning was calculated in order to assess its spatial accuracy. RMSE values were less than twice the pixel size in every case, which allowed a precise overlapping of the mosaics created. Canopy temperature data extracted from thermal images for showed significantly higher temperatures in water stressed trees compared to well irrigated, difference being related to severity of water stress. Thanks to the ultrahigh resolution of remote images obtained (<0.1m spatial resolution for thermal infrared images), and beyond its capacity to delineate efficiently each individual tree, the methodology presented here will also make it possible the analysis of intra-canopy variations and the accurate calculation of vegetation and water stress indices

Acquisition d'images thermiques par drone : corrections radiométriques à partir de données terrain

Thermal images have many applications in agronomy, including crop water stress status assessment. Nowadays, the miniaturization of thermal cameras allows installing them onboard the Unmanned Aerial Vehicles (UAV), but this miniaturization leads to some difficulties: the miniaturized thermal cameras have no temperature control system of their sensor. The instability of the miniaturized camera makes a high drift in the acquisition of temperature data so that acquired thermal images don't fit the real temperature of the studying object, so data have to be continuously corrected. We need to have stable reference on field in order to compute the actual temperature value. In this article we present a method for radiometric correction of UAV remote sensed thermal images. We have implemented a device in order to retrieve ground temperature measurements. This device is composed with four targets (cold, hot, dry soil, wet soil) which measured continuously the target temperature thanks to IR120 (Campbell ®) radio-thermometer. A meteorological station is included in this ground system and acquires air temperature and moisture, solar radiation, wind speed and direction every 10 seconds. The images are radiometrically corrected by linear regression from on ground thermal data collected. Corrected images have been compared with mean canopy surface temperature of a sample of 10 trees measured with radio-thermometers. The results showed a good link between data from on ground radio-thermometer and data from thermal camera after radiometric correction. We can conclude that images obtained by this method are of sufficient quality to be used in vegetation water stress studies. (Résumé d'auteur

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).This study was funded by the projects AGL2015-65351-R, PID2019-109089RB-C31 and TED2021-131606B-C21 of the Spanish Ministry of Economy and Competitiveness. AG-R was funded by a Margarita Salas post-doctoral contract from the Spanish Ministry of Universities affiliated to the Research Vice-Rector of the University of Barcelona. VRRY was funded by a pre-doctoral contract from the Spanish Ministry of Economy and Competitiveness (PRE2020-092369). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.The authors acknowledge the contribution of the CERCA Program (Generalitat de Catalunya). The authors acknowledge Andrea Lopez, Ezequiel Arqué, Jordi Companys, and Josep Millera for their technical contributions to the experimental setup of field trials.info:eu-repo/semantics/publishedVersio

Plant Breeding and Management Strategies to Minimize the Impact of Water Scarcity and Biotic Stress in Cereal Crops under Mediterranean Conditions

Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.info:eu-repo/semantics/publishedVersio

The effect of cancer on the labor supply of employed men over the age of 65

This paper investigates the relationship between cancer diagnosis and the labor supply of employed men over the age of 65. While almost 60% of male cancers are diagnosed in men over the age of 65, no previous research has examined the effect that cancer has on this age group, which is surprising given the relevance of this group to public policy. With data from the Health and Retirement Study, I show that cancer has a significant negative effect on the labor supply of these workers. Using a combination of linear regression models and propensity score matching, I find that respondents who are diagnosed with cancer work 3 fewer hours per week than their non-cancer counterparts. They are also 10 percentage points more likely to stop working. This reduction seems to be driven by a deterioration in physical and mental health

Accuracy test of a miniaturized thermal infrared camera to be carried on board UAVs

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High-throughput field phenotyping of apple tree response to drought by UAV thermal imaging: Supervised image classification for unravelling intra-canopy temperature variation

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresIn the context of climate change, general temperature increase and summer drought periods are expected, notably in the Mediterranean region. Research of tolerant varieties to water stress is therefore relevant regarding future breeding scope. This research aimed at phenotyping the apple tree response to water stress in a field trial where a segregating population (123 hybrids) was submitted to contrasting water regimes. Our assumption was that differences in stomatal closure among progenies can be efficiently estimated through high-resolution airborne imaging, including thermal signature, since reduction in transpiration rate and subsequent loss of latent heat flux is traduced by an elevation of canopy surface temperature. By using Unmanned Aerial Vehicle (UAV) as vector, and multispectral and thermal image acquisition, a series of flights were planned on sunny days with high evaporative demand, at 40m elevation, making it possible to characterize the tree response at individual scale, simultaneously across the trial. Zenithal images were pretreated, i.e. orthorectified and normalized, then georeferenced and mosaicked. Retrieval of spectral values from orthomosaics was performed on tree individuals within a central 60cm radius buffer zone. This allowed computing individual vegetation and water stress indices. Since indices computation relied on composite images, we tested the contribution of supervised classification of thermal images for unravelling well-illuminated leaves from shadowed vegetation or soil pixels. Comparison between different image analysis methods revealed usefulness of classification to improve the interpretation of thermal images, in relation to water stress and genotype effects. It opens the perspective of high-throughput pipelines for image analyses

Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresNumerous agronomical applications of remote sensing have been proposed in recent years, including water stress assessment at field by thermal imagery. The miniaturization of thermal cameras allows carrying them onboard the unmanned aerial vehicles (UAVs), but these systems have no temperature control and, consequently, drifts during data acquisition have to be carefully corrected. This manuscript presents a comprehensive methodology for radiometric correction of UAV remotely-sensed thermal images to obtain (combined with visible and near-infrared data) multispectral ortho-mosaics, as a previous step for further image-based assessment of tree response to water stress. On summer 2013, UAV flights were performed over an apple tree orchard located in Southern France, and 4 dates and 5 h of the day were tested. The 6400 m(2) field plot comprised 520 apple trees, half well-irrigated and half submitted to progressive summer water stress. Temperatures of four different on-ground stable reference targets were continuously measured by thermo-radiometers for radiometric calibration purposes. By using self-developed software, frames were automatically extracted from the thermal video files, and then radiometrically calibrated using the thermal targets data. Once ortho-mosaics were obtained, root mean squared error (RMSE) was calculated. The accuracy obtained allowed multi-temporal mosaic comparison. Results showed a good relationship between calibrated images and on-ground data. Significantly higher canopy temperatures were found in water-stressed trees compared to well-irrigated ones. As high resolution field ortho-mosaics were obtained, comparison between trees opens the possibility of using multispectral data as phenotypic variables for the characterization of individual plant response to drought

Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology

A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%