Soil compaction is one of the main negative factors that limits plant growth and crop yield. Therefore, it is important to determine the soil resistance level and map it for the field to find solutions for the negative effects of the compaction. Nowadays, high powered communication technology and computers help us on this issue within the approach of precision agriculture applications. This study is focused on the design of a penetrometer, which can make instantaneous soil resistance measurements in the soil horizontally and data acquisition software based on the GPS (Global Positioning System). The penetrometer was designed using commercial 3D parametric solid modelling design software. The data acquisition software was developed in Microsoft Visual Basic.NET programming language. After the design of the system, manufacturing and assembly of the system was completed and then a field experiment was carried out. According to the data from GPS and penetration resistance values which are collected in Microsoft SQL Server database, a Kriging method by ArcGIS was used and soil resistance was mapped in the field for a soil depth of 40 cm. During operation, no faults, either in mechanical and software parts, were seen. As a result, soil resistance values of 0.2 MPa and 3 MPa were obtained as minimum and maximum values, respectively. In conclusion, the experimental results showed that the designed system works quite well in the field and the horizontal penetrometer is a practical tool for providing on‑line soil resistance measurements. This study contributes to further research for the development of on-line soil resistance measurements and mapping within the precision agriculture applications.

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R² = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm.

Biomass pellets are widely used for combustion but can also serve as sustainable feedstocks for pyrolysis. This study examined wood (WP), palm-pruning (PP), reed (RD), and daphne (DP) pellets. We present a compact framework linking composition (proximate/ultimate and lignocellulosic fractions) with TG/DTG, FTIR, TGA-derived indices (CPI, D_dev, R_w), T_pmax and R_av to predict product selectivity and temperature ranges. TG/DTG showed the following sequence: hemicellulose (≈200–315 °C) first, cellulose (≈315–400 °C) with a sharp maximum, and lignin ≈200–600 °C. Low-ash WP and DP had sharper, higher peaks, favoring concentrated devolatilization and condensables. Mineral-rich PP and RD began earlier and showed depressed peaks from AAEM catalysis, shifting toward gases and ash-richer chars. Composition shaped these patterns: higher cellulose increased R_av and CPI; links to T_pmax were moderated by ash. Lignin strengthened a high-T shoulder, while hemicellulose promoted early deacetylation (RD’s 1730 cm⁻¹ acetyl C=O) and release of CO₂ and acids. Correlations (|r| ≥ 0.70) supported these links: VM with total (m_∞) and second stage mass loss; cellulose with R_av and CPI (T_pmax moderated by ash); lignin and O/C with T_f and last stage mass loss; ash negatively with T_i, T_pmax, and m_∞. The obtained results guide the sustainable valorization of biomass pellets by selecting temperatures for liquids, H₂/CO-rich gases or low-ash aromatic chars.