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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.

To realize high-efficiency and precision paddy field leveling, a tractor-attached laser-controlled rotary scraper land leveler for paddy fields was designed. An elevation model for the lase...

Leveling technology of paddy fields is an important part of the mechanization of rice production, which ensures the consistent depth of water layers to restrain the growth of weeds and improve the utilization of pesticides and fertilizers. However, when the working distance increases in paddy fields (due to laser energy attenuation and laser spot diffusion), the reliability and efficiency of existing laser-leveling technology decreases. To address those problems, a new leveling technology that uses water surface as a benchmark is introduced. The proposed method of water surface as a benchmark to measure the height of the scraper and a benchmark-Recognition algorithm embedded in the proposed method was developed. Experiments were conducted to validate the effectiveness and accuracy of the proposed method by comparing the results with those of existing methods (height measurement for scraper with laser plane as benchmark). The results obtained by the proposed method have similar trends in variation and higher resolution.