Due to physical, chemical, and biological impediments in the soil, many producers have adopted deep soil preparation as an alternative to correct agronomic adversities, including soil compaction in deeper layers (Feng et al., 2020), replacing subsoiling and plowing with heavy harrowing (Kogut et al., 2016).

Profilometry monitors soil preparation quality, measuring the mobilized cross-sectional area, blistering, average layer thickness, index, and modification of soil roughness (Bögel et al., 2016). The evaluation of these parameters usually involves traditional equipment, such as a rod or slide bar profilometer (Borges et al., 2019). Other tools include contactless devices, such as drone images, optical lasers or digital cameras, ultrasonic sensors, or Lidar (light detection and ranging) sensors (Vasil’ev et al., 2021).

Contactless devices, such as drone images, were reported in the work of Fanigliulo et al. (2020), who compared traditional methods of evaluating soil roughness (laser profilometer) with drone RGB 3D imaging techniques for the evaluation of different soil preparation methods. The use of light drones allowed the replication of the results obtained by traditional methods, introducing advantages in terms of time, repeatability, and analyzed surface, reducing human error during data collection and creating a digital agriculture solution for laborious field monitoring. However, the limitation of flight operating time and data processing are still factors to be studied and solved in this method.

Laskoski et al. (2017) developed, built, and validated a laser profilometer to measure the soil mobilized and elevated areas, the average thickness of the mobilized layer, and blistering after soil preparation. The acquired variables did not present statistical significance compared to the parameters collected by the traditional rod and the developed profilometers. The developed profilometer showed superior performance in the collection, acquisition, and storage of data, in addition to not modifying the structure of the analyzed profile once it is a contactless method. However, this sensor has limitations regarding operation under incident solar radiation and sensitivity in the remittance of electrical signals.

Gilliot et al. (2017) developed a fully automatic photogrammetric approach to measure soil surface roughness from field photos taken with a digital camera sensor without geometric restrictions. These figures calculated 3D soil models with millimetric precision and generated 11 roughness indices implemented in a Python program. The results presented were that two roughness indices, the surface tortuosity index and the average height value, are more efficient in discerning levels of agricultural soil preparation. The authors reported noise problems during the electronic acquisition and the need to create shadows over the camera scanning area.

Ewetumo et al. (2019) developed an automated profilometer with a dual ultrasonic sensor in a tool holder plate equipped with an electronic horizontal displacement system and a central unit with a microcontroller. The results showed that the operating time was lower than 60 sec, with an accuracy and resolution of 1 cm besides the maximum deep range of 200 cm. This sensor’s advantages include the possibility of detecting any material that does not absorb sound and does not influence color in the reading process, but it has the disadvantage that encrusted or accumulated solids can affect the measurement.

Foldager et al. (2019) compared two soil profiling methods, Lidar and conventional metal rods sensor, in measuring the cross-sectional area and groove geometry after soil preparation. Using Lidar, a system generated 3D scans of the soil surface and an average groove geometry allowed comparing the geometric variations along the grooves. The measurements of cross-sectional area and geometries by the rod profilometer and Lidar showed up to 41% difference between the two methods. This Lidar sensor benefits from a low light operation and shows high precision under different rough and textural soil surfaces and low energy consumption, facilitating its use in field conditions. Its limitations are the requirement for more complex electronic circuits, developer experience, and being more susceptible to particulate interference in its optical assembly.

Sampling process automation is often used in agriculture to assist decision-making, minimize the occurrence of process failures, and promote positive decisions (Tian et al., 2020). This tool has numerous benefits in soil profilometry, especially the execution in continuous cycles. Currently, the traditional method used in profilometry is costly and time-consuming. It also generates an inordinate amount of data to post-process, suggesting the need to modernize this system through electronics and automation.

Among the automated models, Polyakov & Nearing (2019) developed a laser profilometer as an alternative for expensive and complex systems. It had a good performance under various conditions compared to the Lidar system which presented overestimated roughness results.

Operational speed and the implement’s operation depth affect soil preparation results. It can contribute to the elevation of the work layer, generating the so-called floating effect. However, this implication on heavy harrows is little studied, considering that their constructive characteristics can minimize the minus effects of increasing operational speed in harrowing operations.

Thus, the objective was to develop an automatic data acquisition system (DAS) for profilometry processes in conference to the traditional method, comparing the effect of four speeds on soil preparation as a verification test.

Once the data showed normality and homogeneity, they underwent an analysis of variance (Table 1). The values of the T-test resulted in no significant difference in the means of the evaluated parameters for the two profilometers tested.

During the field test to evaluate the efficiency of the developed profilometer versus the conventional one, the acquired data classified as normal and homogeneous (Table 2). It was later evaluated by an analysis of variance, indicating that the means of modified roughness and blistering obtained by the two profilometers did not significantly differentiate between themselves. The mechanized set operational speed did not influence the modified roughness variable, as there was no interaction between the evaluated treatments.

For the elevated area, the results of the F-test indicated that the mean values diverged by 38%, significantly to 5%, more for the electronic profilometer when compared to the conventional profilometer (Table 2). It showed a significant effect with the increase in the mechanized set operational speed (Fig. 6A), resulting in a decrease of 138.85 cm² of the elevated area with each one-kilometer per hour addition.

The tested profilometers’ means also diverged statistically in the mobilized area, resulting in a 19% higher reading for the electronic profilometer. The operational set speed did not significantly influence the measurement of the mobilized area, as there were no reports of an interaction between the evaluated treatments (Table 2).

With soil blistering (Fig. 6B), which represents the percentage between the raised area and the mobilized area resulting from the action of the soil preparation implement, we observed that the lowest blistering index occurs at a speed of 9.8 km h^-1. That is due to the tendency of high area reduction with the increasing speed of the discs.

Additionally, a trend of lower values for the soil layer thickness was observed in this experiment, around 25% lower than the electronic profilometer, as shown in Table 2.

The results presented in Table 1 support the validation of the development equipment, indicating its use for the field, corroborating with Laskoski et al. (2017). The developed profilometer did not statistically differ when compared to the traditional ones.

The conventional profilometer presented lower values for the elevated area due to the immersion of the rods on the surface of the mobilized soil (Table 2). The deepening of the stems underestimates the soil surface altimetric values, as described by Laskoski et al. (2017). Considering that this effect does not occur when using the electronic profilometer, the analyzed profile data is more accurate since its reading occurs through laser scanning of the soil surface (Ferenčík et al., 2019).

Speed selection affects the final groove quality, depending on the soil projection (Francetto et al., 2021), and influences the soil blistering index (Fig. 6B). The disk blade deflects the soil particles as a fluid, especially at high operating speeds, corroborating with Ahmadi (2018) when describing grid movements over the ground.

This fact shown in soil thickness (Table 2) is due to the sensitivity of the parameters to the characteristics of the used instrument, including its measurement scale (Martinez‐Agirre et al., 2020). The conventional profilometer presents a biased reading for the elevated area resulting in the decrease of this parameter. The divergences related to soil thickness are due to the insertion of rods of the conventional profilometer into the soil at the time of installation of the equipment. That can also be correlated to lower soil density after the grading operation (Kool et al., 2019), promoting the occurrence of rod insertion into the soil.

With the verified similarity between the traditional and electronic equipment, there is the possibility of optimizing soil preparation monitoring. The monitoring can act as a data source for management plan elaborations and decision-making. It can also help the farmer acknowledge the real soil preparation quality, considering that conventional equipment can underestimate soil surface altimetric values because of the deepening of the reading rods. That does not occur with the developed equipment because its reading is performed indirectly through the laser sensor.

As conclusions, the obtained results demonstrate the efficiency of the developed profilometer in measuring the proposed parameters. It also provides greater data collection agility and operational mobility. The laboratory testing showed no significant difference in the parameters of modified roughness, elevated and mobilized area, blistering, and thickness, denoting the phase validation that indicates applicability in the field. During the field tests, the electronic profilometer presented superior results for the elevated and mobilized area and soil layer thickness. That is due to the absence of interference in the measurements that occur in the conventional profilometer due to the insertion of the rods in the soil. The increase in the mechanized set speed reduced the elevated area and soil blistering. That is due to the rise in the disc rotation and consequent soil particle deviation.