Browsing by Subject "Täsmäviljely"
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(2023)Täsmäviljelyn yleistyminen on lisännyt tarvetta maaperän spatiaalisen vaihtelun kartoittamiselle, ja tämän myötä markkinoille on tullut erilaisia maaperäkartoitusmenetelmiä. Tämän tutkimuksen tavoitteena oli selvittää, pystytäänkö gammasäteilyn mittaamiseen perustuvalla SoilOptix-maaperäkartoitusmenetelmällä estimoida pellon sisäistä pH-arvon, helppoliukoisen fosforin sekä mangaanin vaihtelua. Tutkimus toteutettiin Forssassa sijaitsevalla peltolohkolla, jossa yhteensä 48 mittauspisteen maa-analyysituloksista ja SoilOptix-menetelmän pistemäisistä estimaateista tehtiin korrelaatioanalyysejä ja pistekaavioita. Myös gammasäteilyraakadatan toistettavuutta havainnoitiin visuaalisesti. Työn toinen tavoite oli selvittää, vähensikö peltolohkolle tehty täsmäkalkitus pellon sisäistä satovaihtelua ja ravinnevaihtelua. Tämän selvittämiseksi analysoitiin puimurin satokartoitusaineistoja vuoden 2020 kevätrapsin (Brassica napus ssp. oleifera (Moench) Metzg.) ja vuoden 2022 kevätvehnän (Triticum aestivum L.) puinneista. SoilOptix-menetelmällä ei pystytty estimoida peltolohkon sisäistä vaihtelua minkään koejäsenen osalta. Kalkitus ei oleellisesti vähentänyt peltolohkon sisäistä satovaihtelua, mutta ravinnevaihtelua se vähensi. Eri vuosien gammasäteilyn raakadatassa oli samankaltaisuuksia visuaaliseen havainnointiin pohjautuen. Syyt heikkojen korrelaatioiden taustalla eivät ole ilmeiset, ja ne vaatisivat lisätutkimusta. Erityinen kiinnostuksen kohde olisi menetelmässä kerätyn raakadatan lukuarvot, joita ei tässä tutkimuksessa saatu tarkastella. Johtopäätöksenä todetaan selvä lisätutkimuksen tarve menetelmälle.
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(2020)Technological development has led to a rapid increase of precision agriculture in the last decade. Various sensors can easily be mounted in drones. Monitoring of canopies in different growth stages is therefore quite easily accessible. With this data can different cultivation decisions be made rapidly. In the past remote sensing methods in agriculture have mainly been done with satellite images. The main objective of this research was to determine whether there is a significant difference in accuracy between drone and satellite survey. Hypothesis is that data observed with drone is more accurate, thus there should be noticeable differences in parcel vegetation indices between these two methods. There is a lack of comparative research between these survey methods and usually satellite images have been used only in larger entireties. Two individual parcels used in this study were measured by drones in OPAL-life project in 2016 and 2017. Measuring was made with multi- and hyperspectral cameras and vegetation indices made from these measures were compared with maps made from Sentinel-2 material. Additional comparison was also made between Sentinel-2 based average normalized difference vegetation index and measured grain yield from defined parcels. Results were compared and satellite measurement proved to be quite accurate. NDVI timelines from parcel were almost identical between satellite- and drone images. On the other hand, anomalies and variation in parcel were more observable in drone-based images. Satellite based NDVI pixel values corresponded quite good with drone-based pixel values (R2=0.65). Also, a very significant correlation between vegetation indices and observed grain yields in parcel was observed (R2=0.93) before flag leaf emerging. However the time frame for measurement is very narrow. Results were surprising, but also highlighted the problems involved in this kind of parcel imagery. Satellite images were quite accurate, although some anomalies could not be observed in satellite images. Other issues with surveying formed to be a problem. These were specially the narrow timeframe for measurements, but also clouds were a big obstacle when using satellite images.
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(2021)Soil structure is one of the key elements when it comes to plant growth and yield production. For the last 30 years, the agricultural machines have grown in size, which has increased the stress to soil caused by the machinery. When the stress is high enough to increase the strengths in soil, and the porosity and permeability are decreased, the soil is compacted. There are some soil scanners, which are capable of mapping the soil strengths. The aim of this study was to use the draft data from the CAN bus in this purpose. The topsoil strength data could be then used as part of precision agriculture,for example in problem solving with low-yield areas, and as part of variable depth tillage purposes. The goal for this study was to build a measurement system, which could record the draft data of a tractor and also to find out the usability of this data in precision agriculture. Measurements of the reference data (electric conductivity, organic matter) are also part of this study, which included initialization of the Veris iScan+ and also the development and building the subframe for the scanner. As the measurement system Raspberry Pi minicomputer equipped with CAN bus and GNSS boards was used. The measurement system was programmed with Python programming language. In the measurements, a Valtra N141 tractor and a Kverneland Turbo 2 cultivator were used to tillage the two-hectare test plot. After the measurements, MATLAB and ArcGis were used for processing the raw data, mapping the features, and analysing the data. In this study, the implement draft was found to be significantly higher in the headlands of the field, which face the most field traffic. In the test plot, there were three individual zones with high draft force values, each resulting from different reasons. These zones were visible in the EC and OM maps, but all of the draft force variation could not be explained by the reference data. According to this study, the CAN bus draft data could be used as topsoil strength indicator, and with the right reference data the draft data can be used to map the topsoil compacted areas.
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