Poppy crop height and capsule volume estimation from a single UAS flight

The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R$^2$ values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R$^2$ of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R$^2$ 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry

This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 3 dec 2018, available online: http://www.tandfonline.com/10.1080/10106049.2018.1552322Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTMDSM coupling. Biomass field data confirmed the goodness of fit of the yieldbiomass estimation (R2=0,88 and 1,12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensorsThis work was supported by the life project “Operation CO2: Integrated Agroforestry Practices and Nature Conservation Against Climate Change - LIFE+ 11 ENV/ES/535” and by Xunta de Galicia under the grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” Ref. ED431B 2016/030 and Ref. ED341D R2016/023.S

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Yield prediction by machine learning from UAS‑based mulit‑sensor data fusion in soybean

16 p.Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.SI"Development of Analytical Tools for Drone-based Canopy Phenotyping in Crop Breeding" (American Institute of Food and Agriculture

Is Einkorn Wheat (Triticum monococcum L.) a Better Choice Than Winter Wheat (Triticum aestivum L.)? Wheat Quality Estimation for Sustainable Agriculture Using Vision-Based Digital Image Analysis

Einkorn wheat (Triticum monococcum L. ssp. monococcum) plays an increasingly important role in agriculture, promoted by organic farming. Although the number of comparative studies about modern and ancient types of wheats is increasing, there are still some knowledge gaps about the nutritional and health benefit differences between ancient and modern bread wheats. The aim of the present study was to compare ancient, traditional and modern wheat cultivars—including a field study and a laboratory stress experiment using vision-based digital image analysis—and to assess the feasibility of imaging techniques. Our study shows that modern winter wheat had better yield and grain quality compared to einkorn wheats, but the latter were not far behind; thus the cultivation of various species could provide a diverse and sustainable agriculture which contributes to higher agrobiodiversity. The results also demonstrate that digital image analysis could be a viable alternate method for the real-time estimation of aboveground biomass and for predicting yield and grain quality parameters. Digital area outperformed other digital variables in biomass prediction in relation to drought stress, but height and Feret’s diameter better correlated with yield and grain quality parameters. Based on these results we suggest that the combination of various vision-based methods could improve the performance estimation of modern and ancient types of wheat in a non-destructive and real-time manner

Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping

The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Sh

Les forêts d'arbres décisionnels et la régression linéaire pour étudier les effets du sous-solage et des drains agricoles sur la hauteur des plants de maïs et les nappes d'eau dans un sol à perméabilité réduite

Les travaux de sous-solage qui améliorent le drainage interne et décompactent des horizons rendus pratiquement imperméables par la compaction profonde seraient bénéfiques aux sols de faible perméabilité. Le sous-solage profond exécuté perpendiculairement aux drains avec un bélier (bulldozer) pourrait être plus efficace pour temporairement améliorer le drainage de ces sols qu’une sous-soleuse conventionnelle attelée à un tracteur et opérée en mode parallèle aux drains. Toutefois, les aménagements réalisés pour améliorer le drainage de surface et interne de ces sols rendent complexe l’évaluation de ces pratiques en dispositif expérimental. L’objectif principal de ce projet était de comparer les forêts d’arbres décisionnelles (FAD) à la régression linéaire multiple (RLM) pour détecter les effets du sous-solage et des systèmes de drainage souterrain et de surface sur la hauteur des plants et la profondeur moyenne de la nappe durant la saison de croissance. Un essai de sous solage a été réalisé à l’automne 2014, dans une argilelimoneuse Kamouraska naturellement mal drainée, remodelée en planches arrondies et souffrant de compaction importante. L’essai comparait un témoin sans sous-solage à quatre traitements de sous-solage, soit une sous-soleuse sur bélier ou sur tracteur, opérées parallèlement ou perpendiculairement aux drains. Chaque traitement a été répété trois fois et disposé aléatoirement en autant de blocs. Au printemps 2016, 198 puits ont été creusés à 60 cm de profondeur pour enregistrer la profondeur de la nappe sous chaque traitement entre juin et juillet 2016. La photogrammétrie a été utilisée pour estimer la hauteur des plants de maïs. Les FAD et la RLM permettent de détecter les principaux facteurs affectant la hauteur des plants de maïs et la profondeur moyenne de la nappe, soit les aménagements antérieurs pour améliorer le drainage interne et le drainage de surface des sols. Les coefficients de détermination obtenus avec les FAD (R2 ≥ 0,94) étaient toutefois plus élevés que ceux obtenus avec la RLM (R2 ≥ 0,28). Aucun traitement de sous-solage n’a amélioré significativement le drainage interne ni la hauteur des plants de maïs par rapport au témoin sans sous-solage. Les FAD permettent en outre de mieux visualiser les relations non linéaires entre les variables prédites et les autres variables, notamment la position sur la planche et la distance aux drains souterrains, et finalement de déterminer les distances aux drains souterrains optimales ( 4 m), la distance optimale à la raie de curage (> 8 m) et la profondeur moyenne critique de la nappe (< 0,25 m). Les FAD permettent ainsi de prédire la hauteur des plants de maïs et la profondeur moyenne de la nappe avec une plus grande précision qu’avec la RLM