Use of unmanned aerial vehicles and sensors mounted on them for monitoring crop growth in agriculture

Z razpoložljivimi brezpilotnimi letalniki, nanje nameščenimi ustreznimi senzorji oz. kamerami ter programsko opremo za daljinsko zaznavanje lahko zelo natančno ocenimo stanje posevka. V kmetijstvu sta od kamer najbolj uporabljeni običajna RGB in multispektralna kamera. Senzorje je treba pravilno kalibrirati in slike računalniško popraviti, da lahko iz njih razberemo agronomsko pomembne podatke. Z merjenjem odboja ali emisije elektromagnetnega valovanja od rastlin in vegetacije pridemo do ocen pomembnih sestojnih in fizioloških parametrov, kot so vsebnost vode in hranil v rastlini, višina rastlin, biomasa posevka, dostopnost hranil v tleh, idr. Za interpretacijo podatkov pogosto izračunamo vegetacijske indekse, med katerimi je najbolj široko uporabljen vegetacijski indeks NDVI. V nalogi so predstavljeni primeri uporabe brezpilotnih letalnikov in ustreznih senzorjev pri zaznavanju pomembnih parametrov rasti posevkov, pri čemer so poleg opisa prednosti takih načinov ocene posevkov navedene tudi njihove pomanjkljivosti. Od slednjih je predvsem pomembno zavedanje, da imajo lahko različna stanja posevka in rastlin lahko podobne učinke na spektralni podpis snemane vegetacije. Poleg daljinskega zaznavanja, lahko z brezpilotnimi letalniki opravljamo tudi nekatere agrotehnične ukrepe, kot sta škropljenje in gnojenje.With the available unmanned aerial vehicles (UAVs), appropriate sensors (cameras) installed on them, and remote sensing software, we can assess the condition of the crop very accurately. Of different camera types, conventional RGB and multispectral cameras are the most widely used remote sensing sensors in agriculture. The sensors must be properly calibrated and the images corrected by computer so that agronomically important data can be deduced from them. By measuring the reflection or emission of electromagnetic waves from plants and vegetation, we arrive at estimates of important vegetation and physiological parameters, such as water and nutrient content in the plant, plant height, crop biomass, soil nutrient availability, etc. Vegetation indices are often calculated for data interpretation, among which the NDVI vegetation index is the most widely used. Here, different examples of the use of UAVs and appropriate sensors for the detection of important crop parameters are presented. In addition to describing the advantages of such methods of crop assessment, their shortcomings are also stated. Of the latter, it is particularly important to be aware that different crop and plant conditions can have similar effects on the spectral signature of a given vegetation. In addition to remote sensing, we can also perform some agro-technical measures with UAVs, such as spraying and fertilizing