Mogućnosti procjene prinosa lucerne i djetelinsko-travnih smjesa korištenjem satelitskih snimaka Sentinel-2

master's thesis

• Title (english): Possibilities of yield estimation of alfalfa and clover-grass mixtures using Sentinel-2 satellite images
• Author: Luka Štefančić
• Mentor: Hrvoje Kutnjak (mentor)
• Committee member: Hrvoje Kutnjak (predsjednik povjerenstva)
• Committee member: Josip Leto (član povjerenstva)
• Committee member: Snježana Bolarić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Agriculture; (Department of Field Crops, Forage and Grassland); Zagreb
• Defense date and country: 2023-09-21, Croatia
• Scientific / art field, discipline and subdiscipline: BIOTECHNICAL SCIENCES; Agronomy

Abstract

Precizna poljoprivreda počinje se razvijati 90-tih godina 20. stoljeća kao nova strategija upravljanja procesima poljoprivredne proizvodnje bazirana korištenjem suvremenih računalnih tehnologija i daljinskih istraživanja kojima je omogućeno preciznije i lakše i bolje upravljanje proizvodnjom kroz maksimiziranja omjera troškova i koristi. Daljinska istraživanja su jedan od glavnih izvora podataka u preciznoj poljoprivredi koja korištenjem satelitske tehnologije omogućavaju predviđanje prinosa u poljoprivredi. U dosadašnjoj praksi Satelitski sustav Sentinel-2 se pokazao korisnim alatom za snimanje poljoprivrednih površina. U ovom diplomskom je istražena mogućnosti njegova korištenja za predviđanje prinosa suhe tvari(ST) dviju sorti lucerni i dvije djetelinsko-travne smjese kao o i povezanost njihovih prinosa i vrijednosti normaliziranog indeksa razlike vegetacije (NDVI) dobivenih satelitskim sustavom Sentinel-2 između sezona 2021. i 2022. Pokus je proveden na pokušalištu Maksimir na površini od 1 ha. Sjetva dviju djetelinsko-travnih smjesa i dviju sorti lucerne je provedena na 16 parcela u slučajnom bloknom rasporedu, svaka veličine 25 x 25 m. Kroz dvije godine 2021. 2022. površine su košene tri puta godišnje i tada su prikupljani uzorci s polja i uspoređivani s podacima sa satelitskih snimaka. Utvrđena je korelacija NDVI-a i prinosa ST unutar 2022. godine, ali ne i između 2021. i 2022. godine.

Abstract (english)

Precision agriculture began to develop in the 90s of the 20th century as a new strategy for managing agricultural production processes based on the use of modern computer technologies and remote sensing, which enabled more precise, easier and better production management by maximizing the ratio of costs and benefits. Remote sensing is one of the main sources of data in precision agriculture that, using satellite technology, enables the prediction of yields in agriculture. In practice so far, the Sentinel-2 satellite system has proven to be a useful tool for recording agricultural areas. In this thesis, the possibilities of its use for predicting the dry matter yield (DM) of two varieties of alfalfa and two clover-grass mixtures, as well as the relationship between their yields and the values of the normalized difference of vegetation index (NDVI) obtained by the Sentinel-2 satellite system between the seasons of 2021 and 2022. The experiment was conducted at the Maksimir test site on an area of 1 ha. The sowing of two clover-grass mixtures and two varieties of alfalfa was carried out on 16 plots in a random block layout, each 25 x 25 m in size. During the two years 2021 and 2022, the areas were mowed three times a year and then field samples were collected and compared with data from satellite images. A correlation between NDVI and DM yield was established within 2022, but not between 2021 and 2022.

• Keywords: daljinska istraživanja; predviđanje potencijalnog prinosa; NDVI; suha tvar; precizna poljoprivreda
• Keywords (english): remote sensing; potential yield prediction; NDVI; dry matter; precision agriculture
• Language: croatian
• URN:NBN: urn:nbn:hr:204:126166
• Study programme: Title: Plant Sciences; Study programme type: university; Study level: graduate; Academic / professional title: magistar / magistra inženjer/inženjerka biljnih znanosti (magistar / magistra inženjer/inženjerka biljnih znanosti)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Access restricted to authenticated users
• Created on: 2023-09-22 07:59:38