Precision agriculture or site specific management consider agricultural activities in variety of scales smaller than the field scale all the way down to specific plants. It can be defined as implementing the right treatment at the right time and only where needed. Therefore, remote sensing by its versatile spectral, spatial and temporal resolutions can provide tools for monitoring agricultural lands. Applying remote sensing tools for precision agriculture aims can be beneficial economically as well as environmentally. Here are several topics that were and are studied in our lab:

The yield of dryland wheat is limited by rainfall, therefore, a decision support system (DSS) for improving economic profit and reducing environmental impact was developed. The quick and simplified DSS was developed and it results in the ability to decide whether to apply nitrogen to grain harvest, no additional fertilization and leave for grain harvest or harvest hay (Bonfil et al. 2004). The conditions during the heading stage are reflected in final grain quality and yield. Therefore, in order to apply this DSS on the effective growth stage for analysis the normalized heading index (NHI) was developed (Pimstein et al. 2009).

Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. The majority of the known N spectral indices are indirectly related to N by using chlorophyll sensitive bands. The 1510 nm band is directly related to N content since it is an overtone of the connection between N and hydrogen atoms. It was concluded that combining direct and indirect relation to N content results in better assessment of N content of above ground potato plants (Herrmann et al. 2010).

The importance of potassium (K) and phosphorus (P) contents to wheat yield and grain quality, and the very little experience that has been gained on nutritional monitoring of other than nitrogen using remotely sensed technologies, a study was undertaken to explore the possibility of identifying these mineral stresses using spectral data. Based on the current experiments and observations it can be concluded that monitoring K and P contents of wheat crops can be performed using remotely sensed data. However, the level of accuracy and the type of information that can be retrieved depend on the type of sensor and algorithm to be used, as well as on the kind of monitoring that is required to be implemented (Pimstein et al. 2011).

Leaf Area Index (LAI) is an important variable that governs canopy processes. LAI assessment by the band formations of Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 was explored for potato and wheat fields. Both satellites contain red-edge bands. Since the red-edge inflection point (REIP) is linearly related to LAI values it was concluded that it can assess LAI better than NDVI. Therefore the importance for the future satellites mentioned above (Herrmann et al. 2011).

Weed control is commonly performed by applying selective herbicides homogeneously over entire agricultural fields. However, applying herbicide only where needed could have economic and environmental benefits. Spectral separation between crops and weed has resulted in almost perfect classification results. Spectral separation based on canopy has resulted in similar results (Shapira et al. 2013). Applying hyperspectral imaging on ground level has resulted in spatial separation. The red-edge band was the most important region for the separation models (Herrmann et all.2013).

Spider Two spotted spider mites (TSSM; Tetranychus urticae Koch) cause significant damage to crops and yields, in the field as well as in greenhouses. By feeding, TSSM destroy chloroplast-containing cells; this damage can be spectrally detected in the reflectance of the visible and near-infrared regions. Several spectral indices were calculated and it was resulted that spectral data can separate between early damage and less indices can separate between damage levels (Herrmann et all. 2012). These data now days are analyzed for continuous spectra and preliminary results show ability to separate damage levels by continuous spectra.