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

Regenerating Agricultural Landscapes with Perennial Groundcover for Intensive Crop Production

The Midwestern U.S. landscape is one of the most highly altered and intensively managed ecosystems in the country. The predominant crops grown are maize (Zea mays L.) and soybean [Glycine max (L.) Merr]. They are typically grown as monocrops in a simple yearly rotation or with multiple years of maize (2 to 3) followed by a single year of soybean. This system is highly productive because the crops and management systems have been well adapted to the regional growing conditions through substantial public and private investment. Furthermore, markets and supporting infrastructure are highly developed for both crops. As maize and soybean production have intensified, a number of concerns have arisen due to the unintended environmental impacts on the ecosystem. Many areas across the Midwest are experiencing negative impacts on water quality, soil degradation, and increased flood risk due to changes in regional hydrology. The water quality impacts extend even further downstream. We propose the development of an innovative system for growing maize and soybean with perennial groundcover to recover ecosystem services historically provided naturally by predominantly perennial native plant communities. Reincorporating perennial plants into annual cropping systems has the potential of restoring ecosystem services without negatively impacting grain crop production and offers the prospect of increasing grain crop productivity through improving the biological functioning of the system

Breeding for resilience: a strategy for organic and low-input farming systems?

Global change is increasingly affecting agricultural production and threatening food security. Organic and low-input farming systems are less demanding in fossil energy and might thus contribute to moderating global carbon missions. Moreover, under increased uncertainty and variability in environmental conditions, these systems offer solutions for buffering against climatic extremes, disease epidemics, changing nutrient availability, and other stresses that will add to already heterogeneous environmental conditions. 2010 has been designated the Biodiversity Target year by Parties to the Convention on Biological Diversity. Yet, it is clear that biological diversity in agroecosystems, measured as the number and abundance of species as well as genetic diversity within cultivated plants, is still decreasing, largely due to the negative impacts of intensive industrial agriculture. Overall, ecosystem services delivered by biodiversity such as plant disease control, soil fertility and pollination are jeopardized by its decline. These threats present an opportunity for the organic sector to develop original and innovative strategies for biodiversity preservation and increased resilience in the field. The second EUCARPIA meeting of the Section Organic plant breeding and low-input agriculture organised in Paris, France, from the 1st to the 3rd of December 2010, by INRA – UMR Génétique Végétale Le Moulon and ITAB, wishes to take inspiration from the ecological sciences to highlight the use of biodiversity in agriculture while taking advantage of the new tools coming from genomics. Therefore, the symposium will deal with breeding strategies for organic and low-input farming systems with a special emphasis on approaches that allow for more resilience in response to global change. Some 130 participants representing 20 countries will attend the symposium, including students, researchers and other professionals from universities, institutes, breeding companies, governemental institutions, Non Governemental Organizations and farmers. The programme features 30 oral and 37 poster presentations, covering the following areas: · Improving resilience of agro-ecosystems · Utilizing and conserving agrobiodiversity in agricultural landscape · Global change and adaptability · New insights into the mechanisms of adaptation to local conditions and organic farming · Breeding for diverse environments and products · Regional participatory plant breedin

Comparative analysis between fixed and variable rate nitrogen applications in open-field borettana cultivation

openThis thesis presents a comparative analysis of fixed and variable rate nitrogen applications in open-field Borettana onion cultivation. The study integrates field surveys and remote sensing data, specifically NVDI maps obtained through drone imaging. Six sample collections were conducted at different stages of onion growth. By developing and applying variable rate algorithms, the research aims to evaluate the effectiveness and efficiency of precision farming techniques in optimizing nitrogen fertilizer prescriptions. The results provide valuable insights into the benefits and drawbacks of both fixed and variable rate nitrogen applications, contributing to the advancement of precision farming practices in open-field crop cultivation.This thesis presents a comparative analysis of fixed and variable rate nitrogen applications in open-field Borettana onion cultivation. The study integrates field surveys and remote sensing data, specifically NVDI maps obtained through drone imaging. Six sample collections were conducted at different stages of onion growth. By developing and applying variable rate algorithms, the research aims to evaluate the effectiveness and efficiency of precision farming techniques in optimizing nitrogen fertilizer prescriptions. The results provide valuable insights into the benefits and drawbacks of both fixed and variable rate nitrogen applications, contributing to the advancement of precision farming practices in open-field crop cultivation

Improving the legume-rhizobium symbiosis in Zimbabwean agriculture: A study of rhizobia diversity & symbiotic potential focussed on soybean root nodule bacteria

Legumes are important components for both smallholder and commercial agriculture in Zimbabwe in relation to food and income security and improvement of soil fertility through a symbiotic association with rhizobia. The efficiency of biological nitrogen fixation is largely unknown in most situations in Zimbabwe. While rhizobia inoculant is available for many legumes, only soybean is consistently inoculated. Native soybean rhizobia have not been genetically characterized or taxonomically identified. The inoculation response of cowpea, groundnut, lablab, sunn hemp, pigeon pea and soybean was investigated under Zimbabwean field conditions, together with effects on a subsequent maize crop. Separately, soybean microsymbionts were obtained from soils with known inoculation histories from nine smallholder and three commercial farms to isolate naturalized inoculant strains and native rhizobia. Isolates were genetically characterized using partial recA gene sequences. Phylogenetic analysis of representative isolates was undertaken using recA, glnII, and 16S rDNA sequences. Symbiotic genes nifH and nodC were analysed. Isolates were screened for nitrogen fixation efficiency and the two best fixers per species were tested for compatibility with three soybean varieties under glasshouse conditions. The best isolate of each species was tested across different field sites in Zimbabwe. Inoculation generally increased grain yield, shoot biomass and nitrogen accumulation. Maize biomass was higher when succeeding inoculated legumes than when succeeding uninoculated legumes. Partial recA gene sequencing grouped the isolates into four species: Bradyrhizobium diazoefficiens (13%), B. japonicum (21%), B. elkanii (61%) and B. ottawaense (5%). B. ottawaense had the widest host range across 13 legumes, followed by B. elkanii, B. diazoefficiens and B. japonicum. Phylogenetic analyses were consistent with vertical transmission of core genes and horizontal transfer of symbiotic genes. Based on symbiotic performance and edaphic competence, strains B. japonicum NAZ554 and NAZ710 and B. diazoefficiens NAZ629 were identified as potential elite inoculant strains for soybean in Zimbabwe

Just-in-time Pastureland Trait Estimation for Silage Optimization, under Limited Data Constraints

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge although typically this is assumed to be based around very large datasets. This work hypothesises that through the advantages of state-of-the-art deep learning systems, pastureland crop traits can be accurately assessed in a just-in-time fashion, based on data retrieved from an inexpensive sensor platform, under the constraint of limited amounts of labelled data. However the challenges to achieve this overall goal are great, and for applications such as just-in-time yield and moisture estimation for farm-machinery, this work must bring together systems development, knowledge of good pastureland practice, and also techniques for handling low-volume datasets in a machine learning context. Given these challenges, this thesis makes a number of contributions. The first of these is a comprehensive literature review, relating pastureland traits to ruminant nutrient requirements and exploring trait estimation methods, from contact to remote sensing methods, including details of vegetation indices and the sensors and techniques required to use them. The second major contribution is a high-level specification of a platform for collecting and labelling pastureland data. This includes the collection of four-channel Blue, Green, Red and NIR (VISNIR) images, narrowband data, height and temperature differential, using inexpensive proximal sensors and provides a basis for holistic data analysis. Physical data platforms built around this specification were created to collect and label pastureland data, involving computer scientists, agricultural, mechanical and electronic engineers, and biologists from academia and industry, working with farmers. Using the developed platform and a set of protocols for data collection, a further contribution of this work was the collection of a multi-sensor multimodal dataset for pastureland properties. This was made up of four-channel image data, height data, thermal data, Global Positioning System (GPS) and hyperspectral data, and is available and labelled with biomass (Kg/Ha) and percentage dry matter, ready for use in deep learning. However, the most notable contribution of this work was a systematic investigation of various machine learning methods applied to the collected data in order to maximise model performance under the constraints indicated above. The initial set of models focused on collected hyperspectral datasets. However, due to their relative complexity in real-time deployment, the focus was instead on models that could best leverage image data. The main body of these models centred on image processing methods and, in particular, the use of the so-called Inception Resnet and MobileNet models to predict fresh biomass and percentage dry matter, enhancing performance using data fusion, transfer learning and multi-task learning. Images were subdivided to augment the dataset, using two different patch sizes, resulting in around 10,000 small patches of size 156 x 156 pixels and around 5,000 large patches of size 240 x 240 pixels. Five-fold cross validation was used in all analysis. Prediction accuracy was compared to older mechanisms, albeit using hyperspectral data collected, with no provision made for lighting, humidity or temperature. Hyperspectral labelled data did not produce accurate results when used to calculate Normalized Difference Vegetation Index (NDVI), or to train a neural network (NN), a 1D Convolutional Neural Network (CNN) or Long Short Term Memory (LSTM) models. Potential reasons for this are discussed, including issues around the use of highly sensitive devices in uncontrolled environments. The most accurate prediction came from a multi-modal hybrid model that concatenated output from an Inception ResNet based model, run on RGB data with ImageNet pre-trained RGB weights, output from a residual network trained on NIR data, and LiDAR height data, before fully connected layers, using the small patch dataset with a minimum validation MAPE of 28.23% for fresh biomass and 11.43% for dryness. However, a very similar prediction accuracy resulted from a model that omitted NIR data, thus requiring fewer sensors and training resources, making it more sustainable. Although NIR and temperature differential data were collected and used for analysis, neither improved prediction accuracy, with the Inception ResNet model’s minimum validation MAPE rising to 39.42% when NIR data was added. When both NIR data and temperature differential were added to a multi-task learning Inception ResNet model, it yielded a minimum validation MAPE of 33.32%. As more labelled data are collected, the models can be further trained, enabling sensors on mowers to collect data and give timely trait information to farmers. This technology is also transferable to other crops. Overall, this work should provide a valuable contribution to the smart agriculture research space

South Dakota Agricultural Experiment Station Records

The South Dakota Agricultural Experiment Station\u27s mission is one of the cornerstones of a land grant university. In addition to enhancing the quality of life in our state, our research directly supports the teaching programs offered by the College of Agriculture and Biological Sciences, the College of Education and Human Sciences, and the educational program delivered by SDSU Extension. The collection is composed of material generated by the stations and includes financial reports, general office records, research, institutional reviews, and publications, including some bulletins and circulars. Also included are files related to station biochemistry

CERNAS – Current Evolution and Research Novelty in Agricultural Sustainability

This book addresses original studies and reviews focused on the current evolution and research novelty in agricultural sustainability. New developments are discussed on issues related with quality of soil, natural fertilizers or the sustainable use of land and water. Also crop protection techniques are pivotal for the sustainable food production under the challenges of the Sustainable Development Goals of the United Nations, allied to innovative weed control methodologies, as a way to reduce the utilization of pesticides. The role of precision and smart agriculture is becoming more pertinent as the communication technologies improve at a high rate. Waste management, reuse of agro industrial residues, extension of shelf life and use of new technologies are ways to reduce food waste, all contributing to a higher sustainability of the food supply chains, leading to a more rational use of natural resources. The unquestionable role of bees as pollinators and contributors for biodiversity is subjacent to the work of characterization of beekeeping activities, which in turn contribute, together with the valorization of endemic varieties of plant foods, for the development of local communities. Finally, the short circuits and local food markets have a decisive role in the preservation and enhancement of rural economies.info:eu-repo/semantics/publishedVersio