Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then ... ~\url{www.github.com/PaulAlbert31/Clover_SSL}.Comment: 11 pages, 5 figures. Accepted at the Agriculture-Vision CVPR 2022 Worksho

Mehitamata õhusõiduki rakendamine põllukultuuride saagikuse ja maa harimisviiside tuvastamisel

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.This thesis aims to examine how machine learning (ML) technologies have aided significant advancements in image analysis in the area of precision agriculture. These multimodal computing technologies extend the use of machine learning to a broader spectrum of data collecting and selection for the advancement of agricultural practices (Nawar et al., 2017) These techniques will assist complicated cropping systems with more informed decisions with less human intervention, and provide a scalable framework for incorporating expert knowledge of the PA system. (Chlingaryan et al., 2018). Complexity, on the other hand, can be seen as a disadvantage in crop trials, as machine learning models require training/testing databases, limited areas with insignificant sampling sizes, time and space-specificity, and environmental factor interventions, all of which complicate parameter selection and make using a single empirical model for an entire region impractical. During the early stages of writing this thesis, we used a relatively traditional machine learning method to address the regression problem of crop yield and biomass prediction [(i.e., random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] to predicted dry matter (DM) yields of red clover. It obtained favourable results, however, the choosing of hyperparameters, the lengthy algorithms selection process, data cleaning, and redundant collinearity issues significantly limited the way of the machine learning application. We will further discuss the recent trend of automated machine learning (AutoML) that has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unravelling substance problems. However, a present knowledge gap exists in the integration of machine learning (ML) technology with unmanned aerial systems (UAS) and hyperspectral-based imaging data categorization and regression applications. In this thesis, we explored a state-of-the-art (SOTA) and entirely open-source AutoML framework, Auto-sklearn, which was built on one of the most frequently used machine learning systems, Scikit-learn. It was integrated with two unique AutoML visualization tools to examine the recognition and acceptance of multispectral vegetation indices (VI) data collected from UAS and hyperspectral narrow-band VIs across a varied spectrum of agricultural management practices (AMP). These procedures incorporate soil tillage method (STM), cultivation method (CM), and manure application (MA), and are classified as four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Additionally, they have not been thoroughly evaluated and lack characteristics that are accessible in agriculture remote sensing applications. This thesis further explores the existing gaps in the knowledge base for several critical crop categories and cultivation management methods referring to biomass and yield analysis, as well as to gain a better understanding of the potential for remotely sensed solutions to field-based and multifunctional platforms to meet precision agriculture demands. To overcome these knowledge gaps, this research introduces a rapid, non-destructive, and low-cost framework for field-based biomass and grain yield modelling, as well as the identification of agricultural management practices. The results may aid agronomists and farmers in establishing more accurate agricultural methods and in monitoring environmental conditions more effectively.Doktoritöö eesmärk oli uurida, kuidas masinõppe (MÕ) tehnoloogiad võimaldavad edusamme täppispõllumajanduse valdkonna pildianalüüsis. Multimodaalsed arvutustehnoloogiad laiendavad masinõppe kasutamist põllumajanduses andmete kogumisel ja valimisel (Nawar et al., 2017). Selline täpsemal informatsioonil põhinev tehnoloogia võimaldab keerukate viljelussüsteemide puhul teha otsuseid inimese vähema sekkumisega, ja loob skaleeritava raamistiku täppispõllumajanduse jaoks (Chlingaryan et al., 2018). Põllukultuuride katsete korral on komplekssete masinõppemudelite kasutamine keerukas, sest alad on piiratud ning valimi suurus ei ole piisav; vaja on testandmebaase, kindlaid aja- ja ruumitingimusi ning keskkonnategureid. See komplitseerib parameetrite valikut ning muudab ebapraktiliseks ühe empiirilise mudeli kasutamise terves piirkonnas. Siinse uurimuse algetapis rakendati suhteliselt traditsioonilist masinõppemeetodit, et lahendada saagikuse ja biomassi prognoosimise regressiooniprobleem (otsustusmetsa regression, tugivektori regressioon ja tehisnärvivõrk) punase ristiku prognoositava kuivaine saagikuse suhtes. Saadi sobivaid tulemusi, kuid hüperparameetrite valimine, pikk algoritmide valimisprotsess, andmete puhastamine ja kollineaarsusprobleemid takistasid masinõpet oluliselt. Automatiseeritud masinõppe (AMÕ) uusimate suundumustena rakendatakse tehisintellekti, et lahendada põhiprobleemid automatiseeritud algoritmi valiku ja rakendatava pipeline-mudeli hüperparameetrite optimeerimise abil. Seni napib teadmisi MÕ tehnoloogia integreerimiseks mehitamata õhusõidukite ning hüperspektripõhiste pildiandmete kategoriseerimise ja regressioonirakendustega. Väitekirjas uuriti nüüdisaegset ja avatud lähtekoodiga AMÕ tehnoloogiat Auto-sklearn, mis on ühe enimkasutatava masinõppesüsteemi Scikit-learn edasiarendus. Süsteemiga liideti kaks unikaalset AMÕ visualiseerimisrakendust, et uurida mehitamata õhusõidukiga kogutud andmete multispektraalsete taimkatteindeksite ja hüperspektraalsete kitsaribaandmete taimkatteindeksite tuvastamist ja rakendamist põllumajanduses. Neid võtteid kasutatakse mullaharimisel, kultiveerimisel ja sõnnikuga väetamisel nelja kultuuriga põldudel (punase ristiku rohusegu, suvinisu, herne-kaera segu, suvioder). Neid ei ole põhjalikult hinnatud, samuti ei hõlma need omadusi, mida kasutatatakse põllumajanduses kaugseire rakendustes. Uurimus käsitleb biomassi ja saagikuse seni uurimata analüüsivõimalusi oluliste põllukultuuride ja viljelusmeetodite näitel. Hinnatakse ka kaugseirelahenduste potentsiaali põllupõhiste ja multifunktsionaalsete platvormide kasutamisel täppispõllumajanduses. Uurimus tutvustab kiiret, keskkonna suhtes kahjutut ja mõõduka hinnaga tehnoloogiat põllupõhise biomassi ja teraviljasaagi modelleerimiseks, et leida sobiv viljelusviis. Töö tulemused võimaldavad põllumajandustootjatel ja agronoomidel tõhusamalt valida põllundustehnoloogiaid ning arvestada täpsemalt keskkonnatingimustega.Publication of this thesis is supported by the Estonian University of Life Scieces and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Semi-supervised dry herbage mass estimation using automatic data and synthetic images

Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic pro- cess to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a

Forage biomass estimation using sentinel-2 imagery at high latitudes

Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance

Potential in mixed swards and breeding of tall fescue (Festuca arundinacea Schreb.)

The incentives of this thesis are some of the challenges that grassland production in Belgium are exposed to. First, more dry summer spells are predicted due to climate change (IPPC, 2007). Second, more grass is cut and conserved due to current trends in dairy production. Tall fescue (Festuca arundinacea Schreb.; Fa) is a forage grass species that is expected to cope with these challenges. This thesis had three objectives: 1. to study the agronomy of Fa under Belgian conditions; 2. to develop methods that could be used in breeding to overcome the main disadvatages of Fa; 3. to breed an Fa Variety adapted to Belgian circumstances

The impact of agrivoltaics on crop production

Facing the consequences of global warming and climate change, the reduction of greenhouse gas emissions is one of the most prior tasks of todays society and policymakers. To achieve this, energy generation is currently transformed towards a reduced utilization of fossil fuels and its replacement through an increased expansion of renewable energy sources. In this context, one challenge will be to spare land resources and diminish potential land use conflicts, in particular between food and energy production. An approach to accomplish this, can be the utilization of production-integrated technologies such as agrivoltaic systems (AV). Agrivoltaic systems are photovoltaic systems specifically adapted for its application in combination with agricultural production. For this, AV systems are installed above or on agricultural fields with certain technical adaptions, enabling agricultural production to be continued. First described in 1981, this approach was taken up in the early 2000s with first AV pilot systems being developed. In first experiments in South-France it has been shown, that through the combined utilization of agricultural land for food and energy production, AV can contribute to an increment of total land productivity. While electrical yields can be increased with an increasing density of the photovoltaic modules mounted above, the proportion of light available for the plants grown underneath and consequently also agricultural yields are reduced. The aim of the present work was to examine, whether the results from these first experiments on crop production under AV can also be transferred to conditions in more moderate climates and also account for crops other than the so far investigated ones. The following four research objectives were defined: 1.) To what extent is plant-available radiation reduced by the solar panels of the AV system? 2.) How does this effect parameters of aerial and soil climate? 3.) How do the cultivated crops respond to the altered cropping conditions with regard to plant growth and development? 4.) Which consequences does this have regarding the yields and the chemical composition of the investigated crop-species? In order to examine these research objectives, a field experiment has been established underneath an experimental AV pilot facility in Southwest-Germany, near Lake Constance. Four different types of crops (grass clover, potatoes, celery and winter wheat) have been selected and cultivated underneath the AV system and on an adjacent reference area for comparison within a two-year experiment. Various microclimatic parameters were recorded in a high-resolution monitoring including all investigated crops on both sites. Crop growth and development was monitored in regular intervals during vegetation period. The harvestable yields of both experimental sites, including crop-specific yield components, were recorded and partially supplemented with an analysis of chemical compounds. The results show, that crop production under an APV system is affected in several ways. Under the given climatic conditions, losses in harvestable yields as a consequence of a reduction of crop-available radiation are most likely. Exceptional years such as 2018 suggest however, that cultivation under AV can have advantages for crop production, in particular under dry and hot climatic conditions. In order to fully exploit this potential, the application of the APV thus seems to be most suitable for more dry climatic regions, whereby innovations and developments in AV technology as well as an improved water management can facilitate a further optimization. Regardless of this, potential conflicts of interest with regard to land use cannot be ruled out and require the integration of agrivoltaics in the existing legislation.Um der Erderwärmung und dem damit einhergehenden Klimawandel entgegen zu wirken, ist die Reduktion der Treibhausgasemissionen eines der vordringlichsten Ziele der aktuellen politischen Zielsetzung und zugleich gesamtgesellschaftliche Aufgabe. Als ein Baustein zum Erreichen dieses Ziels wird die Energieerzeugung sukzessive durch eine reduzierte Nutzung fossiler Energieträger und einen zugleich verstärkten Ausbau erneuerbarer Energiequellen umgestellt, um langfristig zu einer Reduktion der Treibhausgasemissionen beizutragen. Eine Herausforderung hierbei ist, die mit diesem Ausbau einhergehenden Flächenverluste auf ein Mindestmaß zu reduzieren und mögliche Flächenkonflikte, insbesondere zwischen Energieund Nahrungsmittelproduktion, zu vermindern. Eine mögliche Maßnahme, um dies auch auf landwirtschaftlichen Flächen zu erreichen, kann die Nutzung produktionsintegrierter Technologien wie der Agri-Photovoltaik (APV) sein. Die Agri-Photovoltaik beschreibt speziell entwickelte Photovoltaikanlagen, welche über oder auf landwirtschaftlichen Nutzflächen installiert werden und durch spezifische technische Modifikationen eine Weiterführung der landwirtschaftlichen bzw. ackerbaulichen Produktion unter der Anlage ermöglichen. Erstmals im Jahr 1981 beschrieben, wurde dieser Ansatz Anfang der 2000er Jahre aufgegriffen und erste APV-Pilotanlagen entwickelt. In ersten Versuchen in Südfrankreich konnte dabei gezeigt werden, dass durch die kombinierte Nutzung der landwirtschaftlichen Flächen für die Energieund Nahrungsmittelproduktion, die APV zu einer Steigerung der Flächenproduktivität beitragen kann. Während die Stromerträge mit steigender Dichte der Photovoltaikmodule zunahmen, sanken zugleich der Anteil des für die Pflanzen verfügbaren Lichts und damit auch die landwirtschaftlichen Erträge. Ziel der vorliegenden Arbeit war zu untersuchen, wie sich diese Ergebnisse aus ersten Anbauversuchen unter APV-Anlagen auch auf die Anbaubedingungen in gemäßigteren Klimaten sowie auf weitere landwirtschaftliche Kulturen übertragen lassen. Die folgenden vier Versuchsfragen wurden definiert: 1.) In welchem Umfang wird die pflanzenverfügbare Sonneneinstrahlung durch die Solarpanele der APV-Anlage reduziert? 2.) Inwiefern werden dabei auch luft- und bodenklimatische Parameter beeinflusst? 3.) Wie reagieren die angebauten Kulturarten auf die veränderten Anbaubedingungen im Hinblick auf das Pflanzenwachstum und die Pflanzenentwicklung? 4.) Welche Folgen hat dies auf die landwirtschaftlichen Erträge sowie die ausgewählten Qualitätsparameter? Zur Untersuchung dieser Versuchsfragen wurde im Jahr 2016 auf einer Praxisfläche ein landwirtschaftlicher Feldversuch unter einer APV-Pilotanlage im Südwesten Deutschlands, Nahe des Bodensees, angelegt. Um die Auswirkungen auf verschiedene Kulturarten zu untersuchen, wurden für den Versuch vier verschiedene Kulturarten (Kleegras, Kartoffeln, Sellerie und Winterweizen) ausgewählt und in zwei Versuchsjahren unter der Anlage sowie aufeiner nahegelegenen Vergleichsfläche ohne APV-Anlage angebaut. In einem engmaschigen, alle Kulturen auf beiden Flächen umfassenden Monitoring wurden verschiedene mikroklimatische Parameter erfasst. Die Pflanzenentwicklung wurde während der Vegetationsperiode in regelmäßigen Abständen bonitiert. Auf beiden Versuchsflächen wurden die Ernteerträge und kulturspezifische Ertragsparameter erfasst und in Teilen durch eine Analyse der Inhaltsstoffe ergänzt. Die Ergebnisse zeigen, dass die APV-Anlage einen deutlichen Einfluss auf die Bewirtschaftung unter der Anlage hat. Unter den gegebenen klimatischen Bedingungen sind dabei Ertragseinbußen infolge der verminderten Sonneneinstrahlung am wahrscheinlichsten. Ausnahmejahre wie das Jahr 2018 zeigen jedoch, dass der Anbau unter einer Anlage insbesondere unter trockenen und heißen Bedingungen Vorteile für die pflanzenbauliche Nutzung haben kann. Um dieses Potential voll auszuschöpfen bietet sich die Anwendung der APV insbesondere für trockenere Klimaregionen an, wobei eine Weiterentwicklung der APVTechnik sowie ein verbessertes Wassermanagement dazu beitragen können, dieses weiter zu optimieren. Ungeachtet dessen sind etwaige Zielkonflikte im Hinblick auf die Flächennutzung nicht auszuschließen und bedürfen der expliziten Regelungen zur Agri-Photovoltaik in der vorhandenen Gesetzgebung

Constraints and opportunities for lucerne (Medicago sativa L.), Caucasian clover (Trifolium ambiguum M. Bieb), and Russell lupin (Lupinus polyphyllus L.) in the high country of New Zealand

This research focused on perennial legumes for the low fertility regions of the New Zealand high country. The aim was to improve production of legume species in these regions. A six-year field experiment at the Lees Valley evaluated the production of lucerne (Medicago sativa L.), Caucasian clover (Trifolium ambiguum M. Bieb.), white clover (T. repens L.), and red clover (T. pratense L.). Low soil pH and phosphorus deficiency were addressed by lime and P fertiliser application, but lucerne persistence was low at this site. Caucasian clover was the most persistent legume with the highest sown species component (93%) and yield (7.8 t DM/ha) in last season. Lucerne yielded 4.2 t DM/ha in Year 1, but this decreased to 2.7 t DM/ha in Year 3, and 1.0 t DM/ha in Year 6. Causes of these extreme responses were examined in a series of pot experiments. In the Lees Valley soil, 85% of lucerne seedlings died, when no lime was applied (pH 5.4, Al> 6.0 mg/kg) compared with 40% of Caucasian clover seedlings. The 2 t/ha lime application, increased lucerne seedling survival to 80% (pH 6.1, Al< 0.03 mg/kg). A similar survival rate was achieved for Caucasian clover with 1.0 t lime/ha. Results showed low pH and high Al content of the Lees Valley soil suppressed the growth of fine roots and nodulation of lucerne, more than Caucasian clover. However, the level of difference in Al tolerance between the two species could not fully explain the field experiment results at the Lees Valley. The main limitation for lucerne persistence was nitrogen deficiency, as a consequence of nodulation failure due to the Al toxicity. Three field experiments were conducted at Glenmore Station and Ashley Dene, to further confirm results. Russell lupin (Lupinus polyphyllus L.) was included in these later experiments as a potential Al tolerant legume. The percentage of nodulated plants for Russell lupin was constantly over 75%, over two years of the field experiments at Glenmore station and Ashley Dene. In contrast, for lucerne and Caucasian clover, nodulation decreased to zero and 25%, respectively over time in the high Al soil > 5 mg/kg at Glenmore station, but not under moderate levels of Al (ca. 3-4 mg/kg soil) at Ashley Dene. Nodule persistence of lupin was a key factor for plant survival in the high Al soil. At Glenmore station, Russell lupin was successfully established into resident vegetation and yielded over 10 t DM/ha in Year 2. Its vigorous growth was supported by consistent nodulation (75%), in the high aluminium content soil. This highlighted its potential to provide a nitrogen fixing perennial legume for environment that is unsuitable for lucerne. Caucasian clover establishment was low in both field experiments, but yielded 4 t DM/ha in Year 2 at Glenmore station. At Ashley Dene the nodulated lucerne plants produced 8.0 t DM/ha, compared with 2.0 t DM/ha in un-nodulated plants. The ¹⁵N natural abundance method showed ca. 70% of the nitrogen in nodulated plants was derived from BNF. This meant 122 kg N/ha of inoculated lucerne was fixed within six months. Thus lucerne was highly reliant on nitrogen fixation in an N deficient soil. Surface applied lime rates >4 t/ha, increased the top-soil pH from 5.2 to 5.8 and decreased exchangeable Al levels to less than 3 mg/kg in 0-75 mm of soil depth, in the Lees Valley and at Glenmore Station. However, toxic Al levels in deeper soil horizons and its variability compromised the efficiency of surface lime application to enable lucerne persistence at both sites. The measurable effects of lime were less obvious in 75-150 mm of soil depth. Similar changes in soil pH and Al levels were measured at Ashley Dene but only 2 t lime/ha was required. Combined results from the field and pot experiments suggested the main limiting for lucerne and Caucasian clover was nitrogen deficiency that should be provided from BNF in these low N and high Al soils. Therefore the fate of applied rhizobia inoculants, and the possibility of competition with any naturalized rhizobia strains inhabiting lucerne and Caucasian clover nodules was assessed. Eight naturalized strains of Sinorhizobium meliloti were identified from lucerne nodules grown in different high country regions. This was the first identification report of any naturalized S. meliloti occupying lucerne nodules in New Zealand. Soil pH and related Al levels affected the contribution of rhizobia strains to occupy lucerne nodules. At soil pH of 5.5 (2.8 mg Al/kg), 50% of the recovered isolates were the commercial strain. This proportion decreased to 6%, as the commercial genotype had been replaced by naturalized S. meliloti strains when the soil pH increased to 6.7 (Al< 0.3 mg/kg). Further studies are therefore required to evaluate competitive ability, nodulation and nitrogen fixation rate of these identified naturalized strains, compared with the commercial strain. In contrast for Caucasian clover, the applied commercial strain was the only identified Rhizobium leguminosarum from nodules, irrespective of sites. This indicated high specificity of Caucasian clover for rhizobia symbiont to nodulate the roots