High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches

Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals

Accurate ground-based measurements of leaf area index (LAI) are needed for validation of remote sensing-based retrievals used in models estimating plant water use, stress, carbon assimilation and other land surface processes. Several methods for indirect LAI estimation with the Plant Canopy Analyzer (PCA, LAI-2200C, LI-COR, Lincoln, NE, USA) were evaluated using destructive (direct) leaf area measurements in three split-canopy vineyards and one double-vertical vineyard in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A method with the sensor facing the canopy, and four readings occurring evenly across the interrow space, had a coefficient of determination (R2) of 0.87 and relative root mean square error (RRMSE) of 16%, when compared to direct LAI measurements via destructive sampling. A previously used method, with the sensor facing down-row, showed lower correlation to direct LAI (R2 = 0.75, RRMSE = 33%) and underestimation which was mitigated by removing the outer sensor rings from analysis. A PCA method is recommended for rapid and accurate LAI estimation in split-canopy vineyards, though local calibration may be required. The method was tested within small units of ground surface area, which compliments high-resolution datasets such as those acquired by small unmanned aerial vehicles. The utility of ground-based LAI measurements to validate remote sensing products is discussed.info:eu-repo/semantics/acceptedVersio

Adaption to rainfall and temperature variability through integration of mungbean in maize cropping

Climate change has threatened global agricultural activities, particularly in tropical and subtropical regions. Rainfed cropping regions have become under more intense risk of crop yield loss and crop failure, especially in upland areas which are also prone to soil erosion. In Thailand, maize is one of the important economic crops and mostly grown in upland areas of northern regions. Maize yield productivity largely depends on the onset of seasonal rainfall. Uncertainty of seasonal rainfall adversely affects maize yield productivity. Therefore, coping strategies are urgently needed to stabilize maize yields under climate variability. In order to identify suitable coping strategies, early maize sowing and maize and mungbean relay cropping were tested on upland fields of northern Thailand. The specific aims of this thesis were (i) monitoring growth and yield performance of maize and mungbean under relay cropping, (ii) testing early maize sowing and maize mungbean relay cropping as coping strategies under rainfall variations (Chapter 2), (iii) testing effects of relay cropping on growth and yield of mungbean under weather variability (Chapter 4), (iv) determining suitable sowing dates under erratic rainfall patterns by using a modelling approach (Chapter 3), and (v) developing a technique for diagnosis of crop water stress in maize by thermal imaging technique (Chapter 5). Specifically, in Chapter 2 early maize planting or relay cropping strategies were assessed for growth and yield performance of maize under heat and drought conditions. Maize planted in July showed, regardless of sole or relay cropping, low grain formation as a consequence of adverse weather conditions during generative growth. However, July-planted maize relay cropping produced higher above ground biomass than July-planted maize sole cropping and early planting of maize in June. Despite unfavourable weather conditions, maize was, at least partly, able to compensate for such effects when relayed cropped, achieving a higher yield compared to maize sole cropping. June-planted maize sole cropping, however, was fully able to escape such a critical phase and achieved the highest grain yield (8.5 Mg ha-1); however, its associated risk with insufficient rain after early rain spells needs to be considered. Relay cropping showed to be an alternative coping strategy to cope with extreme weather as compared to maize sole cropping. However, relay cropping reduced maize growth due to light competition at young stages of maize before mungbean was harvested (Chapter 2). This negative impact of relay cropping is partly off-set by considering of land equivalent ratio (Chapter 4). Land equivalent ratio indicated a beneficial effect of relay cropping over maize and mungbean solecropping (LER = 2.26). During high precipitation, mungbean sole cropping produced higher yield (1.3 Mg ha-1) than mungbean relay cropping (0.7 Mg ha-1). In contrast to the period of low precipitation, mungbean relay cropping used available water more efficiently and was able to establish its plant, while mungbean sole cropping could not fully withstand severe drought and heat. Mulching effects of maize residues conserved soil water which was then available for mungbean to grow under extreme weather condition. WaNuLCAS modelling approaches can be used to support the decision of maize sowing date in northern Thailand to cope with climate change as indicated by goodness of fit of the model validation (R2 = 0.83, EF = -0.61, RMSE = 0.14, ME = 0.16, CRM = 0.02 and CD = 0.56) (Chapter 3) using forty-eight-year of historical rainfall patterns of Phitsanulok province. Only 27.1% of rainfall probability was classified as a normal rainfall condition. Consequently, maize in this region had faced with high rainfall variability. From long term simulation runs, the current maize sowing date led to strong maize yield variation depending on rainfall condition. Early maize sowing i.e. 15 and 30 days before farmers and staggered planting produced higher yield than current farmers practice (mid of July) in most conditions (91.7%). Simulations revealed that water was the most limiting factor affecting maize growth and yield while nutrients (N and P) had only limited impact. Results of the WaNuLCAS model could be used to identify optimal maize planting date in the area prone to soil erosion and climate variation of northern Thailand; however, the model cannot fully account for heat stress. Thermal imaging technique is a useful method for diagnose maize water status. As presented in chapter 5, the developed Crop Water Stress Index (CWSI) using a new approach of wet/dry references revealed a strong relationship between CWSI and stomatal conductance (R2 = 0.82). Our study results established a linear relationship to predict final maize grain yield and CWSI values at 55 DAS as follows Yield = -16.05×CWSI55DAS + 9.646. Both early planting of maize and/or relay cropping with legumes are suitable coping strategies for rainfall variability prone regions. The positive response of early planting and legume relay cropping offers the opportunity of having a short-duration crop as sequential crop, providing an additional source of protein for humans and fostering crop diversification on-site. This leads to a win-win situation for farmers, food security and the environment due to an enhanced sustainability of this cropping system.Der Klimawandel bedroht die Landwirtschaft weltweit, besonders aber in tropischen und subtropischen Regionen. Im Regenfeldbau steigt das Risiko von Ertragsverlusten und Ernteausfällen, insbesondere in Bergregionen, die auch anfällig für Bodenerosion sind. In Thailand ist Mais eine der wichtigsten wirtschaftlichen Nutzpflanzen und wird hauptsächlich in den Hochlandgebieten in Norden des Landes angebaut. Die Produktivität des Maisertrags hängt weitgehend vom Einsetzen der saisonalen Regenfälle ab. Die Unsicherheit der saisonalen Niederschläge wirkt sich negativ auf die Produktivität von Mais aus. Daher werden dringend Strategien benötigt, um die Maiserträge unter Klimaschwankungen zu stabilisieren. Um geeignete Strategien zu identifizieren, wurden auf Hochlandfeldern in Nordthailand eine frühe Maisaussaat und ein Mais- und Mungbohnen-Staffelanbau getestet. Die spezifischen Ziele dieser Arbeit waren (i) die Beobachtung des Wachstums und der Ertragsleistung von Mais und Mungbohnen unter Staffelanbau, (ii) die Prüfung der frühen Maisaussaat und des Mais-Mungbohnen-Staffelanbaus als Strategien zur Stressvermeidung bei Niederschlagsschwankungen (Kapitel 2), (iii) das Testen der Auswirkungen von Staffelanbau auf das Wachstum und den Ertrag von Mungbohnen unter schwankenden Wetterbedingungen (Kapitel 4), (iv) Bestimmung geeigneter Aussaattermine unter erratischen Niederschlagsmustern mit Hilfe eines Modellierungsansatzes (Kapitel 3) und (v) Entwicklung einer Technik zur Diagnose von Wasserstress bei Mais mit Hilfe von Wärmebildtechnik (Kapitel 5). In Kapitel 2 wurden insbesondere die Strategien der frühen Maisaussaat oder des Mais- und Mungbohnen-Staffelanbaus auf die Wachstums- und Ertragsleistung von Mais unter Hitze und Trockenheit untersucht. Mais, der im Juli gepflanzt wurde, zeigte, unabhängig vom Reinanbau oder des Staffelanbaus mit Mungbohnen, eine geringere Kornbildung als Folge ungünstiger Wetterbedingungen während des generativen Wachstums. Allerdings erzeugte der im Juli gepflanzte Mais-Mungbohnen-Staffelanbau eine höhere oberirdische Biomasse als die im Juli gepflanzte Maismonokultur und die frühe Pflanzung von Mais im Juni. Trotz ungünstiger Witterungsbedingungen konnte der Mais im Staffelanbau mit Mungbohnen negative Effekte zumindest teilweise kompensieren und erzielte einen höheren Ertrag im Vergleich zum Juli gesäten Maisreinanbau. Der im Juni gesäte Maisreinanbau konnte sich jedoch einer solchen kritischen Phase vollständig entziehen und erzielte daher den höchsten Kornertrag (8,5 Mg ha-1);allerdings muss das damit verbundene Risiko unzureichender Regenfälle nach frühen Regenperioden berücksichtigt werden. Der Staffelanbau von Mais und Mungbohnen erwies sich als eine sinnvolle Alternative bei extremen Witterungsbedingungen im Vergleich zum Maisreinanbau. Allerdings reduzierte dieser das Wachstum von Mais aufgrund von Lichtkonkurrenz in frühen Wachstumsstadien des Mais vor der Ernte der Mungbohnen (Kapitel 2). Diese negative Auswirkung wird teilweise durch die Berücksichtigung des Flächenäquivalenzverhältnisses (im Englischen mit LER abgekürzt) ausgeglichen (Kapitel 4). Der LER-Wert zeigte einen positiven Effekt des Staffelanbaus gegenüber dem Mais- und Mungbohnenreinanbaus (LER = 2,26). Bei hohen Niederschlägen brachte der Mungbohnen-Alleinanbau höhere Erträge (1,3 Mg ha-1) als der Mungbohnen- Staffelanbau (0,7 Mg ha-1). Im Gegensatz zur Periode mit geringen Niederschlägen nutzte der Mungbohnen-Staffelanbau das verfügbare Wasser effizienter und konnte seine Pflanze etablieren, während der Mungbohnen-Alleinanbau schwerer Trockenheit und Hitze nicht vollständig standhalten konnte. Die Mulchwirkung von Maisresten konservierte das Bodenwasser, das dann für das Wachstum der Mungbohnen unter den extremen Wetterbedingungen zur Verfügung stand. Die Modellierung der getesteten Systeme mit WaNuLCAS kann verwendet werden, um die Entscheidung über den Zeitpunkt der Maisaussaat in Nordthailand zu unterstützen, um mit dem Klimawandel fertig zu werden, wie die Validierung des Models (R2 = 0,83, EF = -0,61, RMSE = 0,14, ME = 0,16, CRM = 0,02 und CD = 0,56) (Kapitel 4) unter Verwendung von historischen Niederschlagsdaten (1970-2018) der Provinz Phitsanulok zeigt. Lediglich 27,1 % der jährlcihen Niederschläge wurde als normale Niederschlagsbedingung eingestuft. Folglich war der Mais in dieser Region mit einer hohen Niederschlagsvariabilität konfrontiert. Aus den Langzeitsimulationsläufen ging hervor, dass der aktuelle Maisaussaattermin zu starken Schwankungen des Maisertrags in Abhängigkeit von den Niederschlagsbedingungen führte. Eine frühe Maisaussaat, d.h. 15 und 30 Tage vor der Aussaat, und eine gestaffelte Aussaat führten unter den meisten Bedingungen zu höheren Erträgen als die derzeitige Praxis der Landwirte (Aussaat Mitte Juli) (91,7%). Die Simulationen zeigten, dass Wasser der am meisten begrenzende Faktor für das Wachstum und den Ertrag von Mais war, während Nährstoffe (N und P) nur einen begrenzten Einfluss hatten. Die Simulationen des WaNuLCAS-Modells können zur Bestimmung des optimalen Maispflanzdatums in für Bodenerosion und Klimaschwankungen anfälligenGebieten Nordthailands zur Prognose und Testen optimaler Saattermine verwendet werden; allerdings kann das Modell den Hitzestress nicht vollständig berücksichtigen. Die Wärmebildtechnik ist eine nützliche Methode zur Diagnose des Wasserstatus von Mais. Wie in Kapitel 5 dargestellt, zeigte der entwickelte Pflanzen-Wasserstress Index (im Englischen CWSI) unter Verwendung eines neuen Ansatzes von Nass/Trockenreferenzen eine starke Beziehung zwischen CWSI und stomatärer Leitfähigkeit (R2 = 0,82). Die Ergebnisse dieser Arbeit ergaben eine lineare Beziehung zur Vorhersage des endgültigen Maiskornertrags und der CWSI-Werte basierend auf Daten die 55 Tage nach der Aussaat(im Englischen DAS) erhoben wurden. Die Gleichung lautet "Ertrag = -16,05×CWSI55DAS + 9,646". Daraus kann gefolgert werden, dass sowohl die frühe Aussaat von Mais als auch der Staffelanbau mit Leguminosen für Regionen, die von Niederschlagsschwankungen betroffen sind, sich als Strategien zur Vermeidung von Stress im Maisanbau eignen. Die positive Reaktion auf die frühe Aussaat und den Leguminosen-Staffelanbau bietet die Möglichkeit, eine kurzlebige Kultur als Zweitkultur im Maisanbau zu etablieren, die eine zusätzliche Proteinquelle für den Menschen darstellt und die Anbaudiversifizierung vor Ort fördert. Dies führt zu einer Win-Win-Situation für die Landwirte, die Ernährungssicherheit und die Umwelt, da die Nachhaltigkeit des Anbausystems Maisanbaus verbessert wird

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

Quantifying corn emergence using UAV imagery and machine learning

Corn (Zea mays L.) is one of the important crops in the United States for animal feed, ethanol production, and human consumption. To maximize the final corn yield, one of the critical factors to consider is to improve the corn emergence uniformity temporally (emergence date) and spatially (plant spacing). Conventionally, the assessment of emergence uniformity usually is performed through visual observation by farmers at selected small plots to represent the whole field, but this is limited by time and labor needed. With the advance of unmanned aerial vehicle (UAV)-based imaging technology and advanced image processing techniques powered by machine learning (ML) and deep learning (DL), a more automatic, non-subjective, precise, and accurate field-scale assessment of emergence uniformity becomes possible. Previous studies had demonstrated the success of crop emergence uniformity using UAV imagery, specifically at fields with simple soil background. There is no research having investigated the feasibility of UAV imagery in the corn emergence assessment at fields of conservation agriculture that are covered with cover crops or residues to improve soil health and sustainability. The overall goal of this research was to develop a fast and accurate method for the assessment of corn emergence using UAV imagery, ML and DL techniques. The pertinent information is essential for corn production early and in-season decision making as well as agronomy research. The research comprised three main studies, including Study 1: quantifying corn emergence date using UAV imagery and a ML model; Study 2: estimating corn stand count in different cropping systems (CS) using UAV images and DL; and Study 3: estimating and mapping corn emergence under different planting depths. Two case studies extended Study 3 to field-scale applications by relating emergence uniformity derived from the developed method to planting depths treatments and estimating final yield. For all studies, the primary imagery data were collected using a consumer-grade UAV equipped with a red-green-blue (RGB) camera at a flight height of approximate 10 m above ground level. The imagery data had a ground sampling distance (GSD) of 0.55 - 3.00 mm pixel-1 that was sufficient to detect small size seedlings. In addition, a UAV multispectral camera was used to capture corn plants at early growth stages (V4, V6, and V7) in case studies to extract plant reflectance (vegetation indices, VIs) as plant growth variation indicators. Random forest (RF) ML models were used to classify the corn emergence date based on the days after emergence (DAE) to time of assessment and estimate yield. The DL models, U-Net and ResNet18, were used to segment corn seedlings from UAV images and estimate emergence parameters, including plant density, average DAE (DAEmean), and plant spacing standard deviation (PSstd), respectively. Results from Study 1 indicated that individual corn plant quantification using UAV imagery and a RF ML model achieved moderate classification accuracies of 0.20 - 0.49 that increased to 0.55 - 0.88 when DAE classification was expanded to be within a 3-day window. In Study 2, the precision for image segmentation by the U-Net model was [greater than or equal to] 0.81 for all CS, resulting in high accuracies in estimating plant density (R2 [greater than or equal to] 0.92; RMSE [less than or equal to] 0.48 plants m-1). Then, the ResNet18 model in Study 3 was able to estimate emergence parameters with high accuracies (0.97, 0.95, and 0.73 for plant density, DAEmean, and PSstd, respectively). Case studies showed that crop emergence maps and evaluation in field conditions indicated an expected trend of decreasing plant density and DAEmean with increasing planting depths and opposite results for PSstd. However, mixed trends were found for emergence parameters among planting depths at different replications and across the N-S direction of the fields. For yield estimation, emergence data alone did not show any relation with final yield (R2 = 0.01, RMSE = 720 kg ha-1). The combination of VIs from all the growth stages was only able to estimate yield with R2 of 0.34 and RMSE of 560 kg ha-1. In summary, this research demonstrated the success of UAV imagery and ML/DL techniques in assessing and mapping corn emergence at fields practicing all or some components of conservation agriculture. The findings give more insights for future agronomic and breeding studies in providing field-scale crop emergence evaluations as affected by treatments and management as well as relating emergence assessment to final yield. In addition, these emergence evaluations may be useful for commercial companies when needing justification for developing new technologies relating to precision planting to crop performance. For commercial crop production, more comprehensive emergence maps (in terms of temporal and spatial uniformity) will help to make better replanting or early management decisions. Further enhancement of the methods such as more validation studies in different locations and years as well as development of interactive frameworks will establish a more automatic, robust, precise, accurate, and 'ready-to-use' approach in estimating and mapping crop emergence uniformity.Includes bibliographical references

Simulating canopy dynamics, productivity and water balance of annual crops from field to regional scales

2016 Summer.Includes bibliographical references.To provide better understanding of natural processes and predictions for decision support, dynamic models have been used to assess impact of climate, soils and management on crop production, water use, and other responses from field to regional scales. It is important to continue to improve the prediction accuracy and increase the reliability. In this work, we first improved the DayCent ecosystem model by developing a new empirical method for simulating green leaf area index (GLAI) of annual crops. Its performance has been validated using experimental observations from different experimental field locations as well as more aggregate NASS yield data spanning the country. Additionally, sensitivity and uncertainty of important parts of the crop growth model have been quantified. Our results showed the new model provided reliable predictions on crop GLAI, biomass, grain yield, evapotranspiration (ET), and soil water content (SWC) at field scale at various locations. At national scale, the predictions of grain yields were generally accurate with the model capable of representing the geographically-distributed differences in crop yields due to climate, soil, and management. The results indicated that the model is capable of providing insightful predictions for use in management and policy decision making. Although there are challenges to be addressed, our results indicate that the DayCent model can be a valuable tool to assess crop yield changes and other agroecosystem processes under scenarios of climate change in the future