The objective of this paper is to present an overview of applied remote sensing technologies in agriculture and biosystems engineering in the last decade. Satellite based remote sensing applications were - and still are - applied whereas airborne applications and UAV technology were also in the focus of our late research interest. Various sensors were applied for different purposes: multispectral images from satellite platforms as well as a hyperspectral imaging system operated on an airplane was investigated in the early phase of our monitoring work. Recently multispectral imaging in RGB and NIR channels have been applied on various UAV's. The aim of the research in the early stages were to determine the role of multispectral and hyperspectral based vegetationindices for prodicting yield and grain quality of spring barley in Hungary. Spring barley is commonly used as a raw material for beer production. In order to fulfil the quality expectations of the beer industry, grain protein content prediction and measurement plays an important role in spring barley production and marketability. Multispectral vegetation indices were based on Landsat satellite images, meanwhile hyperspectral indices were based on AISA DUAL Airborne Hyperspectral Imaging Systems. In order to be able to compare data with the calculated indices, yield data was collected during harvest (AgroCom Terminal and Yield Mapping System). Quality data (protein content) was collected by two methods: (a) by band in a systematic grid and analyzed in a laboratory; (b) during harvest by Zeltex On-Combine Grain Analyzer. All collected data was converted to 25 by 25 m and 1 by 1 m pixel size maps by means of interpolation techniques (ArcGIS). Results showed that prediction of grain quality compared to Quantity was achievable with higher confidence in both cases. Correlation between multispectral vegetation indices and yield was in the best case r=-0.5854/n=206/, meanwhile between hyperspectral vegetation indices and yield correlation showed much lower correlation. At the same time correlation between multispectral vegetation indices and grain protein content was r=-0,8118/n=206/, while between hyperspectral vegetation indices and protein content /hand collected samples/ the best result was r=-0.5033. An Airborne measurement campaing war carried out in 2009, where for precision crop production, images were collected prior to harvest in July, and site specific data collection was carried out in order to collect field data about winter wheat yield as well asprotein content. Data collected during harvest by means of on-line sensors was interpolated into the same resolution map as the hyperspectral image. Later a second hyperspectral image was taken with the aim of investigation of the applicability of the technology for soil management. At the same time soil samples were collected from an approximately 16 hectare field. Soil moisture measurement was carried out by means of gravimetric and TDR methods. Furthermore, apparent soil electrical conductivity (ECa) was measured by Veris Techniologies on-line ECa mapping instrument. Geostatistical analysis was carried out in order to compare the different data layers. In the last five years interest in UAV's as carriers has grown enormously worldwide, while sensor technology is developing rapidly. Images during a winter wheat vegetation period and maize vegetation period were taken by mean of RGB and multiSPEC 4C multispectral imaging system (Airinov Inc.). UAV based images were compared with various data collected during the vegetation periods. In the last part of the paper results of UAV based image analysis are reported.