Bayesian calibration for multiple source regression model

In large variety of practical applications, using information from different sources or different kind of data is a reasonable demand. The problem of studying multiple source data can be represented as a multi-task learning problem, and then the information from one source can help to study the information from the other source by extracting a shared common structure. From the other hand, parameter evaluations obtained from various sources can be confused and conflicting. This paper proposes a Bayesian based approach to calibrate data obtained from different sources and to solve nonlinear regression problem in the presence of heteroscedastisity of the multiple-source model. An efficient algorithm is developed for implementation. Using analytical and simulation studies, it is shown that the proposed Bayesian calibration improves the convergence rate of the algorithm and precision of the model. The theoretical results are supported by a synthetic example, and a real-world problem, namely, modeling unsteady pitching moment coefficient of aircraft, for which a recurrent neural network is constructed

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

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