To alleviate soil compaction, subsoiling practices using subsoiler implements are commonly implemented. However, subsoiler bodies are subjected to great draft forces because they work deep in the soil. Therefore, to contribute to draft force reduction, in this work, a bio-inspired attack edge for a subsoiler body based on the internal and external contour lines of the claws of the Mexican ground squirrel (Spermophilus mexicanus) is proposed. As a first step, computational fluid dynamic (CFD) modeling was used to select the best bionic subsoiler (BS) according to the draft force requirements. Then, the BS was fabricated and field-evaluated, and its real draft force during tillage was contrasted with those of a curve subsoiler (CS) and a straight subsoiler (SS). The field evaluation demonstrated that the BS demands, on average, $12.37\%$ and $22.25\%$ less draft force than the CS and SS, respectively. Additionally, the BS was better at entering the soil since its mean tillage depths were $24.86\%$ and $5.73\%$ higher than those of the SS and CS geometries, respectively. Therefore, it was found that modeling the attacking edge of a subsoiler body after the Mexican ground squirrel clearly reduced the draft force during tillage.

This study presents the development and evaluation of a portable multisensor system for generating three-dimensional maps of the Normalized Difference Vegetation Index (NDVI) by integrating spectral and geometric information for vegetation monitoring in precision agriculture applications. The system integrates an RGB camera, a near-infrared camera (NIR; Arducam UC-844), and a depth sensor (Intel RealSense D435i), controlled by a Raspberry Pi 5 platform. The main methodological contribution lies in the design of a custom calibration pattern that enables the establishment of geometric correspondences among the sensors and the association of spectral values with points in the three-dimensional point cloud. Extrinsic calibration between the depth sensor and the RGB and NIR cameras yielded mean reprojection errors of 1.26 and 1.43 pixels, respectively. NDVI was calculated for each point in the 3D point cloud by projecting its coordinates onto the RGB and NIR images, incorporating a radiometric correction factor $\alpha$ estimated through a controlled leaf-drying experiment. System performance was evaluated by comparison with measurements from a GreenSeeker HCS-250 reference sensor, yielding correlation coefficients ranging from 0.94 to 0.98 and an overall coefficient of determination of R² = 0.9629. The results support the feasibility of integrating spectral and geometric information to generate three-dimensional NDVI maps with high agreement with reference measurements. The proposed system provides a portable alternative for spatially representing variability in vegetation condition and exploring its application in crop monitoring tasks within precision agriculture.