Prostorna predikcija udjela teških metala u tlima kontinentalne Hrvatske usporedbom metoda strojnog učenja i prostorne interpolacije

Soil contamination caused by heavy metals presents a potential long-term issue to human health and biodiversity due to the bioaccumulation effect. Previous research at the micro level in Croatia detected soil contamination caused by heavy metals above maximum permitted values, which also implied the necessity of their current spatial representation at the macro level in Croatia. The aim of this study was to provide a spatial prediction of six heavy metals considered as contaminants of soils in continental Croatia using two approaches: a conventional approach based on interpolation and a machine learning approach. The prediction was performed on the most recent available data on cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) concentrations in soils, from the Ministry of environment and energy. The conventional prediction approach consisted of the interpolation using the ordinary kriging (OK) in case of input data normality and stationarity, alongside the inverse distance weighting (IDW) method. For the machine learning approach, random forest (RF) and support vector machine (SVM) methods were used. IDW outperformed RF and SVM prediction results for all soil heavy metals contents, primarily due to sparse soil sampling. Soil Cr contents were predicted above the maximum allowed limit, while elevated soil contamination levels in some parts of the study area were detected for Ni and Zn. The highest soil contamination levels were observed in the urban areas of generalized land cover classes, indicating a necessity for its monitoring and the adjustment of land-use management plans.Onečišćenje tla uzrokovano teškim metalima uzrokuje potencijalno dugoročnu opasnost za zdravlje ljudi i biološku raznolikost zbog učinka bioakumulacije. Prethodna istraživanja na mikro razini u Hrvatskoj otkrila su onečišćenje tla teškim metalima iznad maksimalno dopuštenih vrijednosti, što je ujedno impliciralo potrebu poznavanja njihove trenutne prostorne zastupljenosti na makro razini u Hrvatskoj. Cilj ovog istraživanja bio je provesti prostorno predviđanje šest teških metala u tlu koji se smatraju onečišćujućima u kontinentalnoj Hrvatskoj koristeći dva pristupa: konvencionalni pristup zasnovan na interpolaciji i pristup strojnog učenja. Predviđanje je provedeno na najnovijim dostupnim uzorcima tla kadmija (Cd), kroma (Cr), bakra (Cu), nikla (Ni), olova (Pb) i cinka (Zn), prikupljenim od strane Ministarstva zaštite okoliša i energetike. Konvencionalni pristup predviđanja sastojao se od interpolacije korištenjem uobičajenog kriginga (OK) u slučaju normalnosti i stacionarnosti ulaznih podataka, zajedno s metodom inverzne udaljenosti (IDW). Za pristup strojnog učenja korištene su metoda slučajnih šuma (RF) i metoda vektora podrške (SVM). IDW je nadmašio rezultate predviđanja RF i SVM za sve sadržaje teških metala u tlu, prvenstveno zbog nedovoljno gustog uzorkovanja tla. Sadržaj Cr u tlu predviđen je iznad najveće dopuštene granice, dok su za Ni i Zn utvrđene opasne razine onečišćenja tla na dijelovima istraživanog područja. Najveće razine onečišćenja tla zabilježene su u urbanim područjima generaliziranih klasa zemljišnog pokrova, što ukazuje na potrebu za njegovim praćenjem i prilagođavanjem planova upravljanja korištenjem zemljišta

Prostorna predikcija udjela teških metala u tlima kontinentalne Hrvatske usporedbom metoda strojnog učenja i prostorne interpolacije

Soil contamination caused by heavy metals presents a potential long-term issue to human health and biodiversity due to the bioaccumulation effect. Previous research at the micro level in Croatia detected soil contamination caused by heavy metals above maximum permitted values, which also implied the necessity of their current spatial representation at the macro level in Croatia. The aim of this study was to provide a spatial prediction of six heavy metals considered as contaminants of soils in continental Croatia using two approaches: a conventional approach based on interpolation and a machine learning approach. The prediction was performed on the most recent available data on cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) concentrations in soils, from the Ministry of environment and energy. The conventional prediction approach consisted of the interpolation using the ordinary kriging (OK) in case of input data normality and stationarity, alongside the inverse distance weighting (IDW) method. For the machine learning approach, random forest (RF) and support vector machine (SVM) methods were used. IDW outperformed RF and SVM prediction results for all soil heavy metals contents, primarily due to sparse soil sampling. Soil Cr contents were predicted above the maximum allowed limit, while elevated soil contamination levels in some parts of the study area were detected for Ni and Zn. The highest soil contamination levels were observed in the urban areas of generalized land cover classes, indicating a necessity for its monitoring and the adjustment of land-use management plans.Onečišćenje tla uzrokovano teškim metalima uzrokuje potencijalno dugoročnu opasnost za zdravlje ljudi i biološku raznolikost zbog učinka bioakumulacije. Prethodna istraživanja na mikro razini u Hrvatskoj otkrila su onečišćenje tla teškim metalima iznad maksimalno dopuštenih vrijednosti, što je ujedno impliciralo potrebu poznavanja njihove trenutne prostorne zastupljenosti na makro razini u Hrvatskoj. Cilj ovog istraživanja bio je provesti prostorno predviđanje šest teških metala u tlu koji se smatraju onečišćujućima u kontinentalnoj Hrvatskoj koristeći dva pristupa: konvencionalni pristup zasnovan na interpolaciji i pristup strojnog učenja. Predviđanje je provedeno na najnovijim dostupnim uzorcima tla kadmija (Cd), kroma (Cr), bakra (Cu), nikla (Ni), olova (Pb) i cinka (Zn), prikupljenim od strane Ministarstva zaštite okoliša i energetike. Konvencionalni pristup predviđanja sastojao se od interpolacije korištenjem uobičajenog kriginga (OK) u slučaju normalnosti i stacionarnosti ulaznih podataka, zajedno s metodom inverzne udaljenosti (IDW). Za pristup strojnog učenja korištene su metoda slučajnih šuma (RF) i metoda vektora podrške (SVM). IDW je nadmašio rezultate predviđanja RF i SVM za sve sadržaje teških metala u tlu, prvenstveno zbog nedovoljno gustog uzorkovanja tla. Sadržaj Cr u tlu predviđen je iznad najveće dopuštene granice, dok su za Ni i Zn utvrđene opasne razine onečišćenja tla na dijelovima istraživanog područja. Najveće razine onečišćenja tla zabilježene su u urbanim područjima generaliziranih klasa zemljišnog pokrova, što ukazuje na potrebu za njegovim praćenjem i prilagođavanjem planova upravljanja korištenjem zemljišta

REMEDIJACIJA POLJOPRIVREDNOGA ZEMLJIŠTA ONEČIŠĆENOG TEŠKIM METALIMA

The presence of heavy metals in an agricultural land is the primary cause of food product toxicity of a herbal and animal origin associated with a contaminated agricultural land. The anthropogenic sources of pollution, especially the fertilizers and pesticides in agriculture, are the primary sources of agricultural land contamination with heavy metals. The heavy metals whose monitoring is prescribed by the current legislation of the Republic of Croatia include cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb) and zinc (Zn). The aim of this paper is to provide a review of heavy metals that cause contamination of an agricultural land, as well as a review of remediation technologies applied to reduce contamination. Furthermore, the paper considers three groups of remediation technologies, i.e., the biological, chemical, and physical ones, analyzing the applicability, efficiency, cost-effectiveness and accessibility in Croatia to encourage their wider implementation. The biological remediation technologies, also known as phytoremediation, met the set criteria the most, which currently renders them most applicable to the mildly‐ and moderately‐contaminated agricultural land. The chemical and physical remediation technologies are generally more suitable for the remediation of a severely contaminated agricultural land, applied individually or in combination with the phytoremediation methods due to the high cost.Prisutnost teških metala na poljoprivrednome zemljištu primarni je uzrok toksičnosti prehrambenih proizvoda biljnoga i životinjskog podrijetla povezanih s onečišćenim poljoprivrednim zemljištem. Antropogeni izvori onečišćenja, posebno primjena umjetnih gnojiva i pesticida u ratarstvu, primarni su izvor onečišćenja poljoprivrednoga zemljišta teškim metalima. Teški metali čije je praćenje (monitoring) propisano važećom zakonskom regulativom Republike Hrvatske uključuju kadmij (Cd), krom (Cr), bakar (Cu), živu (Hg), nikal (Ni), olovo (Pb) i cink (Zn). Cilj rada bio je dati pregled teških metala koji uzrokuju onečišćenje poljoprivrednoga zemljišta, kao i remedijacijskih tehnologija koje se primjenjuju za smanjenje onečišćenja. U radu su razmatrane tri skupine remedijacijskih tehnologija, biološke, kemijske i fizikalne, i to sa stajališta primjenjivosti, učinkovitosti i ekonomičnosti te sa stajališta društvene prihvatljivosti i dostupnosti u Hrvatskoj, kako bi se potaknula njihova šira implementacija. Biološke remedijacijske tehnologije, poglavito fitoremedijacija, najbolje su zadovoljile postavljene kriterije, što ih trenutačno čini najprimjenjivijima za nisko i umjereno onečišćena poljoprivredna zemljišta. Kemijske i fizikalne remedijacijske tehnologije općenito su pogodnije za remedijaciju teže onečišćenoga poljoprivrednog zemljišta, primijenjene samostalno ili u kombinaciji s metodama fitoremedijacije zbog visokih troškova

A Comparison of Precise Fertilization Prescription Rates to a Conventional Approach Based on the Open Source GIS Software

Fertilization is one of the most important components of precision agriculture, ensuring high and stable crop yields. The process of spatial interpolation of soil sample data is recognized as a reliable method of determining the prescription rates for precise fertilization. However, the application of a free open-source geographic information system (GIS) software was often overlooked in the process. In this study, a method of precise fertilization prescription map creation was developed using an open-source GIS software to enable a wider and cheaper availability of its application. The study area covered three independent locations in Osijek-Baranja County. A method was developed for the fertilization of sugar beet with phosphorous pentoxide, but its application is universal with regard to the crop type. An ordinary kriging was determined as an optimal interpolation method for spatial interpolation, with the mean RMSE of 1.8754 and R2of 0.6955. By comparing the precision fertilization prescription rates to a conventional approach, the differences of 4.1 kg ha-1 for Location 1, 15.8 kg ha-1 for Location 2, and 11.2 kg ha-1 for Location 3 were observed. These values indicate a general deficit in soil phosphorous pentoxide, and precise fertilization could ensure its optimal content in the future sowing seasons

Određivanje pogodnosti poljoprivrednog zemljišta za uzgoj kukuruza (Zea mays L.) primjenom višerazinske GIS multikriterijske analize u kontinentalnoj Hrvatskoj

Previous research in continental Croatia indicated that cropland suitability levels are highly variable and existing natural resources could be better utilized. To overcome this issue, a method of multilevel Geographic information system (GIS)-based multicriteria analysis based on Analytic hierarchy process (AHP) for maize cropland suitability determination was proposed and evaluated. Free spatial data and free open-source software were implemented in the research in 250 m spatial resolution. The effects of air temperature, precipitation, soil and topography were modelled with the multilevel approach to avoid inconsistency in pairwise comparison due to criteria count. The maize suitability index (MSI) was calculated using the weighted linear combination, with higher values indicating proportionally higher suitability. According to the results of the AHP method, the weight consistency ratio was lower than the borderline value (0.042 < 0.100). Soil type and mean air temperature in June had the most impact on the suitability result, with 14.3% and 13.6% influence, respectively. The highest MSI values were achieved along the Sava River, especially in the proximity of Sisak and Nova Gradiška, resulting up to 3.9 of the possible 5.0. The sensitivity analysis of criteria indicated precipitation data in May, July and August particularly impactful in continental Croatia, while mean air temperature in September had very low variability of the suitability and was the least impactful on calculated MSI values.Prethodna istraživanja u kontinentalnoj Hrvatskoj pokazala su da su razine pogodnosti poljoprivrednog zemljišta vrlo promjenjive i da bi se postojeći prirodni resursi mogli iskoristiti na bolji način. Da bi se prevladalo ovo pitanje, predložena je i evaluirana metoda višerazinske GIS multikriterijske analize temeljena na analitičkom hijerarhijskom procesu (AHP) za utvrđivanje pogodnosti poljoprivrednog zemljišta za uzgoj kukuruza. Istraživanje je temeljeno na besplatnim prostornim podacima i besplatnom GIS softveru otvorenog koda s prostornom razlučivost od 250 m. Kriteriji temperature zraka, oborina, tla i topografije modelirani su višerazinskim pristupom kako bi se izbjegla nedosljednost u usporedbi parova kriterija. Indeks pogodnosti područja za uzgoj kukuruza (MSI) izračunat je primjenom težinske linearne kombinacije, pri čemu veće vrijednosti proporcionalno ukazuju na veću pogodnost. Prema rezultatima AHP metode, omjer konzistencije težina bio je niži od granične vrijednosti (0,042 <0,100). Vrsta tla i srednja temperatura zraka u lipnju najviše su utjecali na rezultat pogodnosti, s 14,3%, odnosno 13,6% utjecaja. Najveće MSI vrijednosti postignute su uz rijeku Savu, posebno u okolici Siska i Nove Gradiške, rezultirajući s 3,9 of mogućih 5,0. Analiza osjetljivosti kriterija ukazala je da su podaci o oborinama u svibnju, srpnju i kolovozu bili posebno utjecajni u kontinentalnoj Hrvatskoj, dok je srednja temperatura zraka u rujnu imala vrlo nisku varijabilnost pogodnosti i najmanje je utjecala na izračunate MSI vrijednosti

Trenutni status i perspektiva primjene daljinskih istraživanja u upravljanju poljoprivrednim usjevima

Knowledge of the spatial distribution of agricultural crops and crop rotation is necessary for the understanding of the farming practices concerning the long-term sustainability of agricultural production. Agricultural crops are increasingly subject to drought due to the effects of global climate change, and the same is true for Croatia due to the constant rise in mean annual temperatures and uneven rainfall distribution. Remote sensing methods have proven to be superior in the detection and monitoring of drought compared to conventional methods of observation from meteorological stations. Information on the condition of crops in the early stages of development indicates potential irregularities in the development of agricultural crops. The objective of this paper is to provide a perspective for the application of remote sensing in crop management using state-of-the-art methods. Analysis of the possible implementation of these methods in Croatia was performed on a macro- and micro-level. Spatial classification, cropland suitability multicriteria analysis, drought assessment, weed detection and crop density calculation were evaluated according to the necessary equipment and data processing segments. Remote sensing application in crop management offers a potential basis for better crop management both at the macro-level for land use planning and at the micro-level for family farms.Poznavanje prostorne raspodjele poljoprivrednih usjeva i plodoreda neophodno je za razumijevanje poljoprivredne prakse za dugoročnu održivost poljoprivredne proizvodnje. Poljoprivredni usjevi su sve više izloženi suši zbog učinaka globalnih klimatskih promjena, a isto vrijedi i za Hrvatsku zbog stalnog porasta srednjih godišnjih temperatura i neujednačenoj količini padalina. Metode daljinskog istraživanja pokazale su se superiornima u otkrivanju i praćenju suše u usporedbi s konvencionalnim metodama promatranja s meteoroloških postaja. Podaci o stanju usjeva u ranim fazama razvoja ukazuju na potencijalne nepravilnosti u razvoju poljoprivrednih usjeva. Cilj ovog rada je pružiti perspektivu primjene daljinskog istraživanja u upravljanju usjevima korištenjem najsuvremenijih metoda. Analiza moguće primjene ovih metoda u Hrvatskoj provedena je na makro i mikro razini. Prostorna klasifikacija, multikriterijska analiza pogodnost usjeva, procjena suše, otkrivanje korova i izračun gustoće usjeva analizirani su u odnosu na potrebnu opremu i segmente obrade podataka. Primjena daljinskog istraživanja u upravljanju usjevima nudi potencijalnu osnovu za bolje upravljanje usjevima kako na makro razini za planiranje uporabe zemljišta, tako i na mikro razini za obiteljska poljoprivredna gospodarstva

Sensors and Their Application in Precision Agriculture

The paper depicts sensors in precision agriculture. It encompasses the most significant and frequently used sensors in agriculture. Furthermore, the paper explains the main sensor types according to their design, the recorded range of electromagnetic spectrum, as well as the way of detection, recording, measuring, and representation of the detected energy. The development of remote research has provided deeper understanding of remote sensors and their advantages. The sensors installed on soil testing equipment, fertilizing and crop protection machinery, as well as crop picking machinery have been analyzed relative to precision farming. The paper depicts widely known sensors OptRx, ISARIA and VRT technology. The results of the paper assess the data collected by sensors and processed in order to produce maps for agrotechnical operations. The application of maps decreases the employment of human resources, heightens the capacity of data collection, increases the precision of agricultural activities, and finally results in decreasing the cost of final products. The technological progress over the past decade has enabled the development of technology with variable application standards (VRT) that, according to current needs, enables input optimization

Planiranje nasada lijeske uporabom GIS-a i multikriterijske analize

Spatial and environmental conditions on the agricultural land are invariable components and any plantation planning should take them into consideration. The conducted research presented methodology for suitability calculation of hazel plantations based on multicriteria analysis, performed in Vukovar-Srijem County. Nine criteria representing topographic, climate, pedology and infrastructure properties were modelled in GIS environment. Values of created layers were standardized using stepwise standardization and their respective weights were calculated by Analytical Hierarchical Process. These values were integrated using weighted linear combination, resulting with suitability values. The surrounding area of the City of Ilok had the highest suitability for hazel plantation in the studied locality, with maximum suitability 4.1 out of 5.0. Suitability was visualized on a thematic map, which enables farmers to interpret the data.Poljoprivredno je zemljište stacionarna komponenta s prostornim i ekološkim uvjetima na koje se ne može utjecati, kao što su topografija i klima. Provedeno istraživanje predstavlja metodologiju za izračun pogodnosti nasada lijeske temeljenu na multikriterijskoj analizi i primijenjenu u Vukovarsko-srijemskoj županiji. Devet kriterija koji predstavljaju topografska, klimatska, pedološka i infrastrukturna svojstva modelirana su u GIS okruženju. Vrijednosti stvorenih slojeva standardizirane su pomoću postupne standardizacije, a njihove težine izračunate su analitičkim hijerarhijskim procesom. Te su vrijednosti integrirane pomoću težinske linearne kombinacije, što je rezultiralo vrijednostima pogodnosti. Područje najviše pogodnosti za nasade lijeske na području istraživanja okolica je grada Iloka, s maksimalnom prikladnošću 4,1 od mogućih 5,0. Razina pogodnosti vizualizirana je u obliku tematske karte, koja omogućuje poljoprivrednicima interpretaciju podataka

ANALIZA RGB I MULTISPEKTRALNE KAMERE NA BESPILOTNOME ZRAKOPLOVU ZA KLASIFIKACIJU KUKURUZA STROJNIM UČENJEM

This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural parcel near Koška. The highest overall accuracy was obtained in the combination of the red edge (RE), near-infrared (NIR), and normalized difference vegetation index (NDVI) in both subsets, with a 99.8% and 91.8% overall accuracy, respectively. The conducted analysis proved that the RGB camera obtained sufficient accuracy and was an acceptable solution to the soil and vegetation classification. Additionally, a multispectral camera and spectral analysis allowed for a more detailed analysis, primarily of the spectrally similar areas. Thus, this procedure represents a basis for both the crop density calculation and weed detection while deploying an unmanned aerial vehicle. To ensure crop classification effectiveness in practical application, it is necessary to further integrate the weed classes in the current vegetation class and separate them into crop and weed classes.U ovoj studiji istražena je klasifikacija usjeva i tla korištenjem algoritma strojnoga učenja Random Forest, temeljenoga na crveno-zeleno-plavoj (RGB) i multispektralnoj kameri integriranoj na bespilotnome zrakoplovu. Područje istraživanja obuhvaćalo je dva podskupa poljoprivredne čestice kukuruza dimenzija 10 x 10 m u blizini Koške. Najveća ukupna točnost klasifikacije postignuta je u kombinaciji rubnoga crvenog (RE), bliskoga infracrvenog (NIR) kanala i indeksa normalizirane vegetacijske razlike (NDVI) u oba podskupa, s ukupnom točnošću od 99,8 %, odnosno 91,8 %. Provedena analiza pokazala je da je RGB kamera postigla dovoljnu točnost i da je prihvatljivo rješenje za klasifikaciju tla i vegetacije. Međutim, multispektralna kamera i spektralna analiza omogućile su detaljniju analizu, prvenstveno za spektralno slična područja. Ovaj je postupak temelj i za izračun gustoće usjeva i za otkrivanje korova s pomoću bespilotnih zrakoplova. Kako bi se osigurala učinkovitost klasifikacije usjeva u praktičnoj primjeni, potrebno je dodatno uključiti klase korova u trenutačnu klasu vegetacije i podijeliti ih na klase usjeva i korova