Assessment of vegetation índices derived from UAV images for predicting biometric variables in bean during ripening stage

Here, we report the prediction of vegetative stages variables of canary bean crop employing RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight vegetation indices derived from UAV images from the canary bean, which were evaluated by multiple regression models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations

Prediction of biometric variables through multispectral images obtained from UAV in beans (Phaseolus vulgaris L.) during ripening stage

Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations

Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.info:eu-repo/semantics/publishedVersio

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production

Advances in high throughput and affordable phenotyping for adapting maize and wheat to climate change

[eng] Supplying sufficient food to an increasing population is one of the most important challenges over the next century. To meet this demand, crop productivity will need to increase while it is being threatened by climate change effects like the increase of temperatures and the intensity of drought periods. Improving crop performance is key for an efficient adaptation to these challenging growing conditions, with crop breeding being one of the pillars. In that sense selecting more productive varieties for specific environments requires a better understanding of plant acclimation to stress conditions, including efficient phenotyping approaches. Plant phenotyping research pursues the development of new methods with high-throughput capacity and affordable to characterize non-destructively plant traits of interest. The main focus of this thesis was to develop and study versatile and precise methodologies with high-throughput capacity in order to improve crop performance assessments, while saving time and costs in the phenotyping tasksof two of the most important cereal crops: maize and wheat. The use of unmanned aerial vehicles (UAV) equipped with imaging sensors (including RGB, multispectral and thermal) permits covering simultaneously hectares of experimental fields fast, precisely, and in a non-destructive way. However, ground evaluations may still be an alternative in terms of cost and spatial resolution. The performance of these methodologies to assess genotypic differences in grain yield was evaluated in maize and wheat under different agronomical and environmental growing conditions such as nutrient deficiency, conservation agriculture, drought and heat stress. On one side, maize studies were performed in trials in Zimbabwe focused on the evaluation of genotypes under either low and normal phosphorus conditions or the application of conservation agriculture together with different top-dressing nitrogen fertilization regimes, to overcome the nutrient poverty of soils. In these studies, vegetation indices, related to parameters informing on the above-ground biomass and assessed during early stages of development, performed well as grain yield indicators. Moreover, during more advanced phenological stages, indices informing on the leaf and the canopy color were the traits that reported a better association with grain yield and N content in leaves. For the case of wheat, evaluations were performed in different latitudes in Spain covering a range of environments and grown under different management conditions, and sampling was performed during the reproductive stage (heading, anthesis and grain filling). In general terms, biomass indicators, such as canopy green biomass inferred from vegetation indices, together with water status indicators, such as canopy temperature, were the most critical traits predicting GY. The delay of senescence in water-limited environments and the photosynthetic efficiency measured by multispectral indices like the photochemical reflectance index (PRI) during anthesis were also relevant traits for GY under the rainfed and late-planting trials, respectively.[cat] La producció de suficient aliment per a una població cada cop més gran és un dels reptes més importants per al pròxim segle. Per assolir la demanda, la productivitat dels cultius han d’augmentar alhora que fan front als efectes del canvi climàtic com increment de les temperatures i la intensitat dels períodes de sequera. La millora de la capacitat dels cultius és un element clau per a l’adaptació a aquestes condicions més exigents i la selecció de varietats més productives sota ambients específics requereix una millor comprensió de l’aclimatació dels cultius als estressos. La recerca en fenotipatge de cultius té com objectiu el desenvolupament de noves metodologies d’alt rendiment capaces de caracteritzar característiques d’interès de les plantes d’una manera no destructiva. Sota condicions de camp, l’aplicació de metodologies tradicionals en experiments grans laboriós i requereix molt de temps. El principal objectiu d’aquesta tesi ha estat el desenvolupament i estudi diferents metodologies de caràcter versàtil, precises i d’alta capacitat per a millorar les mesures de com es desenvolupen els cultius, alhora de que es redueixen els costos i el temps requerit per a fer els mostrejos. El treball es basa en dos dels principals cereals: el blat i el blat de moro. L’ús de vehicles aeris no tripulats (UAV, del anglès Unmanned Aerial Vehicles) equipats amb càmeres i sensors (RGB, multiespectrals i termals) permet mesurar simultàniament hectàrees de camps experimentals d’una manera ràpida, precisa i sense la destrucció de mostra. Tot i així, les mesures a nivell de terra també són una alternativa prou potent pel que fa el cost i la resolució espacial. La capacitat d’aquestes metodologies per a mesurar diferencies genotípiques en el rendiment del blat de moro i el blat ha estat analitzada sota diferents condicions de creixement com la deficiència de nutrients, pràctiques de agricultura de conservació, sequera i altes temperatures. Per una banda, els estudis de blat de moro es van desenvolupar a Zimbabwe i estaven focalitzats en l’avaluació de genotips sota condicions diferents de fòsfor o en l’aplicació de l’agricultura de conservació per combatre la pobresa mineral dels sòls. En aquests estudis, les mesures relacionades amb paràmetres de biomassa aèria durant estadis primerencs de desenvolupament va funcionar bé com a indicadors de rendiment. A més, durant estadis fenològics més avançats, mesures de color de la capçada del cultiu van estar associats tant amb el rendiment com amb el contingut de nitrogen en les fulles. En el cas del blat, les avaluacions es van dur a terme a diferents latituds d’Espanya, cobrint un ampli rang de condicions climàtiques i agronòmiques. Els mostrejos es van realitzar en diferents estadis fenològics. En termes generals, els indicadors de biomassa i d’estat hídric del cultiu han estat de les mesures més correlacionades amb el rendiment. L’endarreriment de la senescència del cultiu en els ambients on l’aigua era el factor més limitant i el potencial fotosintètic mesurat per index multiespectrals durant la floració del cultiu han estat rellevants sota condicions de sequera i sembra tardana, respectivament