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In recent years, increasing attention has been paid to the unevenness of farmlands since uneven land could reduce the working quality and service life of agricultural machinery. Due to the special constitution of paddy fields, the prevalent road profilers, such as laser range finders or 3D cameras, are unable to profile the bottom layer on which tractor wheels travel. Based on the concept that the acceleration of a working tractor is a direct response to an uneven path, a novel acquisition platform consisting of a real time kinematic global satellite system (RTK–GNSS), an attitude and heading reference system (AHRS) and a rice-transplanter was developed. Then, the corresponding data processing method was proposed in order to describe the unevenness in terms of the displacement power spectral density (DPSD), and the characteristic parameter Gd(n0) was determined in order to classify the unevenness. Experiments on a cement path, a gravel path and a paddy field path were carried out to validate the proposed methodology, and the results showed the following. (1) The travelling speed of the transplanter had little effect on the unevenness classification. (2) The unevenness classifications for the tested cement, gravel, and paddy field paths were class A, B and C, respectively, in terms of the ISO 8608 standard, which was in accordance with the driver’s perception. (3) For all the applied paths, the calculated ω values of the PSD were greater than 2, which indicated that long waves were prevalent in the spatial frequency range of interest. In summary, the proposed approach is an efficient and effective way to acquire, describe and classify the unevenness of the bottom layer of paddy fields. This work can serve as a basis for future studies of soil structure monitoring, components’ reliability design and adaptive controller development for intelligent farm machinery.

The agricultural machinery implements in paddy field are required to measure the roll angle dynamically. In order to reduce the effects of external acceleration on roll estimation thus improving dynamic measurement precision with low cost, this paper proposed an algorithm, the external acceleration roll angle Kalman filter (EARAKF), which estimated the external acceleration model with a one-order low pass filter and Gaussian white noise. After subtracting the estimated real roll angle from the value of accelerometers, the external acceleration superimposing on the single-axis accelerometer was estimated. Eventually, the roll angle estimation without external acceleration effect was obtained after the estimated external acceleration being subtracted from the Kalman filter (KF) in the measurement equation. Furthermore, a parallel track platform was used to impose external acceleration on the accelerometer. The test results shown that the acceleration on Y-axis increased significantly. The root mean square error (RMSE) of the EARAKF was 0.49°, while that of the KF algorithm was 1.91°, which demonstrated that the EARAKF was less influenced by the external acceleration. In the paddy field tests, the RMSE of the KF algorithm were figured out as 1.74°, while that of the EARAKF algorithm was 0.77°, which concluded that the EARAKF can estimate the external acceleration of the accelerometer and obtain a more accurate roll angle. The proposed algorithm presented a roll angle measurement method for the agricultural implements which working in paddy field dynamic scenario and could get less affected by the external acceleration with low cost and high accuracy.

The elevation detection accuracy of the laser receiver in the laser levelling control system directly affects land-levelling operations. To effectively improve the effect of levelling operations and meet the requirements for the accuracy of elevation detection in different industries, this study optimization designed a multilevel adjustable laser receiver. First, we examined the laser signal detection technology and processing circuit, designed the photoelectric conversion array for the detection of the rotating laser, and converted it into a photocurrent signal. We also designed the filter, amplifier, and shaping and stretching circuits for analogue-to-digital conversion of the photocurrent signal. The digital signal was calculated based on the deviation of the elevation by using a microprocessor and was output by a controller area network (CAN) bus. The laser beam spot diameter transmission and diffusion were then studied, and with the detectable spot diameters were compared and analyzed. Accordingly, an algorithm was proposed to calculate the deviation of laser receiver elevation. The resolution of the elevation deviation was set to &plusmn, 3 mm, however, this value could be adjusted to &plusmn, 6 mm, &plusmn, 9 mm, &plusmn, 12 mm, and &plusmn, 15 mm, according to the requirements. Finally, the laser receiver was tested and analyzed, and the test results of the elevation detection accuracy showed that when the laser receiver was within a radius of 90 m, the elevation detection accuracy was within the &plusmn, 3 mm range. The outcomes of the farmland-levelling test showed that the standard deviation S d of the field surface decreased from 9.54 cm before levelling to 2.42 cm after levelling, and the percentage of sampling points associated with absolute errors of &le, 3 cm was 84.06%. These outcomes meet the requirements of high-standard farmland construction. The test results of concrete levelling showed that within a radius of 30 m, the standard deviation S d of the elevation adjustment of the left laser receiver was 1.389 mm, and the standard deviation S d of the elevation adjustment of the right laser receiver was 1.316 mm. Furthermore, the percentage of the sampling points associated with absolute elevation adjustment errors of &le, 3 mm in the cases of the two laser receivers was 100% after levelling, whereas the standard deviation S d of the sand bed surface was 0.881 mm. Additionally, the percentage of the sampling points associated with absolute errors of &le, 3 mm was 100%. This met the construction standards of the concrete industry.