Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency.

The use of Remotely Piloted Aircraft (RPA) for spraying coffee crops has expanded due to their practicality and cost reduction. This study aimed to evaluate spray rate effects on coffee crops using two RPA (T10 and T20). The study was conducted on a commercial farm with 10-year-old Coffea arabica Catucaí Amarelo. Two aircraft were used, T1 (hydraulic) and T2 (rotary nozzles). The application rates were established at 25 and 50 L ha⁻¹. The application quality was obtained by attaching Water-Sensitive Papers (WSPs) to the upper, middle, and lower parts of coffee trees, inside and outside the plants, in addition to the inter-row areas. The statistical Nested Crossed Design was applied to analyze the dataset for the experimental field with three distinct factors (RPA, application rate, and WSP position) and four replications. WSP position was the most determinant factor across all design effects, followed by RPA. The external layers of leaves received more droplets than internal parts of coffee trees. The WSP position information indicated that no droplets reached the middle interior parts of the plants or underneath them. The inter-row positions (soil) received significantly more drops than the coffee plants, regardless of application rate or RPA. The potential for drift to the soil was high in both applications. The Potential Drift Risks were more significant for RPA T2. Future studies may deepen understanding of the relationship between coverage and specific application models for coffee farming, as traditional application methods require improvements.

Efficient irrigation management is crucial for optimizing crop development while minimizing resource use. This study aimed to assess the spatial variability of water distribution under conventional sprinkler irrigation, alongside soil moisture and infiltration dynamics, using multispectral sensors onboard Remotely Piloted Aircraft (RPAs). The experiment was conducted over a 466.2 m² area equipped with 65 georeferenced collectors spaced at 3 m intervals. Soil data were collected through volumetric rings (0–5 cm), auger sampling (30–40 cm), and 65 measurements of penetration resistance down to 60 cm. Four RPA flights were performed at 20 min intervals post-irrigation to generate NDVI and NDWI indices. NDWI values decreased from 0.03 to −0.02, indicating surface moisture reduction due to infiltration and evaporation, corroborated by gravimetric moisture decline from 0.194 g/g to 0.191 g/g. Penetration resistance exceeded 2400 kPa at 30 cm depth, while bulk density ranged from 1.30 to 1.50 g/cm³. Geostatistical methods, including Inverse Distance Weighting and Ordinary Kriging, revealed non-uniform water distribution and subsurface compaction zones. The integration of spectral indices within situ measurements proved effective in characterizing irrigation system performance, offering a robust approach for calibration and precision water management.

The common bean (Phaseolus vulgaris L.) is an important food crop in tropical agriculture. However, fertilization and soil management methods for common beans require further investigation to reduce production costs and increase sustainability. Furthermore, cultivation methods can directly affect GHG emissions. Thus, this study evaluates the CO₂ emissions from fuel consumption as a function of soil management and fertilization methods on the common bean crop. The randomized block design was used in a 2 × 3 factorial scheme with six repetitions composed of two fertilization systems (spread and furrow) and three soil management systems: convention-al tillage—CT, minimum tillage—MT, and no-tillage—NT. The productive performance of common beans varies according to fertilization methods and soil management. Field capacity in the (CT) was impaired due to the various mechanized soil preparation operations with 0.30 and 0.32 ha h⁻¹, without a significant effect from the fertilization method. CT system resulted in higher CO₂ emissions of 175.74 kg ha⁻¹ and 165.50 kg ha⁻¹; thus, in soil conservation management, these same values were up to 58% lower, with the lowest rates for NT. Crop yield in the MT system presented the best result compared to the CT and NT, with an appropriate cost–benefit ratio for bean production in tropical crops.

Coffee fruit drying is an essential step, as it directly affects the drink’s final quality. The presence of green fruits in this process significantly reduces the quality standard. Conventional characterization techniques are performed manually, are time-consuming, and lead to increased estimation errors. Thus, the objective was to identify green, red, and black fruits in the drying patio by digital classification of multispectral and RGB images. The data were obtained by remotely piloted aircraft during daily flights over coffee fruits for eight days of drying. The analyses involved color segregation techniques, supervised classification (Random Forest), and the application of thirty vegetation indices to RGB and multispectral images, utilizing Python, RStudio, and GIS to generate statistical models and maps, in conjunction with manual fruit estimation and monitoring of temperature and humidity. The use of aerial images proved effective in monitoring colors only during the first three days, due to rapid moisture loss. In all analyses, green fruits showed better spectral separation. Image segmentation proved to be the superior method, adequate up to the third day. Random Forest showed reduced performance due to shadows and color mixing and was only effective on the first day. Among the RGB indices, ExGR and MGRVI stood out, with high potential for detecting green fruits, while TGI indicated two classes of green, highlighting the efficiency of conventional sensors. For multispectral images, NDVI was effective on the first day, and NDWI was effective up to the third day, influenced by fruit of humidity reduction. The need to develop a specific vegetative index to standardize this monitoring is emphasized, based on green separation, considering humidity and robust quality indicators.

Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m² area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially.