An appropriate contact mechanics model and parameters are key to achieving accurate results in discrete element analyzis. This is necessary to predict the process of contact collision between the soil and maize seed during deposition. In this paper, the contact process between maize seed and soil is analyzed using the maize seed variety (Liangyu 99) and maize-sowing field soil (with three different moisture contents) as research objects. Based on this, the contact process between maize seeds and soil has been analyzed, on the basis of which a mechanical model suitable for simulating the contact process between maize seeds and soil has been explored, and the selection of parameters between heterogeneous particles (maize seed and soil particles) has been investigated. The results showed that adhesion forces have a significant effect on the collision process between seed and soil particles. While the presence of tangential adhesion force can be replaced by increasing the static and rolling friction coefficients, the normal adhesion force cannot be compensated in this way. The Edinburgh Elasto-Plastic Adhesive (EEPA) model is selected in this paper to describe the contact between seed and soil particles. The significance of the input parameters in the EEPA model is investigated using the Plackett–Burman test. The parameters between soil and seed particles are optimized using the central composite design, and the optimal parameter combinations are obtained. The relative error between the simulation and test result of the slope test for the three soil moisture contents is within 5.4%, validating the accuracy of the calibrated parameters.

During the operation of a shaped hole seed-metering device, poor seed-filling quality and inconsistent seed-casting points lead to poor seed spacing uniformity, especially in a one-chamber double-row seed-metering device. To solve this problem, a soybean double-row seed-metering device with double-beveled seed guide groove was designed to ensure a high single-seed rate and seed-casting point consistency. Through the theoretical analysis of the working process of the seed-metering device, dynamic and kinematic models of the seeds were established, and the main structural parameters of the seed discharge ring, triage convex ridge, shaped hole, and seed guide groove were determined. The main factors affecting the seeding performance were obtained as the following: the inclination angle of the triage convex ridge, the radius of the shaped hole, and the depth of the seed guide groove. A single-factor test was carried out by discrete element simulation to obtain the inclination angle of the triage convex ridge α3 = 29°, the radius of the shaped hole r1 = 4.16–4.5 mm, and the depth of the seed guide groove l1 = 0.49–1.89 mm. A two-factor, five-level, second-order, orthogonal rotation combination test was conducted to further optimize the structural parameters of the seed-metering device. The two test factors were the radius of the shaped hole and the depth of the seed guide groove, and the evaluation indices were the qualified rate, replay rate, and missed seeding rate. The results showed that the optimal combinations of the structural parameters were the radius of the shaped hole r1 = 4.33 mm and the depth of the seed guide groove l1 = 1.20 mm. Subsequent bench testing demonstrated that the seed discharge’s qualified rate was above 94% at operating speeds of 6–10 km/h, and the seeding performance was stable. The final results of the soil trench test showed that the seed-metering device exhibited a qualified rate of 93.31%, replay rate of 2.04%, and missed seeding rate of 4.65% at an operating speed of 8 km/h. This research outcome may serve as a valuable reference and source of inspiration for the innovative design of precision seed-metering devices.

The double-disc opener of maize precision seeder is an important component which affects sowing quality. After the double-disc opening operation, there will be many unfavorable phenomena such as a W-shaped bottom with pointed ridge, returning soil to furrow, loose and rough furrow sidewall, and large soil blocks in the furrow bottom. These phenomena often cause the problems of poor sowing depth consistency and seed spacing uniformity. In order to solve the above problems, the furrow compaction device with opener was designed to compact and reshape the original seed furrow, eventually forming a smooth and flat V-shaped seed furrow. Through theoretical calculations and kinematic analysis, the main structural parameters of the device were limited to a small range: the spring stiffness coefficient k=0.96-4.19 N/mm and the angle of the furrow compaction wheel φ=30°-60°. In the soil-bin experiment, the rotary combination design was adopted to study the effects of the parameters of the furrow compaction device with opener on the seeds location variation. The regression model of two factors with respect to each indicator was established in the Design-Expert software, revealing the effects of two factors on the indicators. Finally, the optimal structural parameters obtained were: the spring stiffness coefficient k=4.0 N/mm, and the angle of furrow compaction wheel φ=42.4°. The field test was carried out to verify the effect of the furrow compaction device with opener on the performance of precision seeder. The results showed that the average values of the sowing depth variable coefficient, the lateral deviation and the seed spacing variable coefficient respectively were 5.77%, 5.1 mm and 9.54% in the treatment of the furrow compaction device with opener. All indicators were superior to the traditional double-disc opener. This research can provide references for the design of furrow opening device and maize precision seeder. Keywords: furrow opener, seed furrow, compaction, sowing depth variable coefficient, lateral deviation, seed spacing variable coefficient DOI: 10.25165/j.ijabe.20201302.4941 Citation: Wang W J, Diao P S, Jia H L, Chen Y L. Design and experiment evaluation of furrow compaction device with opener for maize. Int J Agric & Biol Eng, 2020; 13(2): 123–131.

Twin-row ridge cultivation is widely used in soybean planting in northern China. In order to find the optimal parameters of soybean agronomy, the twin-row planter with subsoiling was designed. Field experiments were conducted to evaluate the effects of plant arrangements and cluster densities on soybean growth and grain yield under different tillage treatments. The experiment used a randomised complete block design consisting of 20 treatments in a 2×2×5 factorial arrangement. Two tillage treatments were inter-row subsoiling and no subsoiling. Each tillage treatment included the combination of plant arrangements (side-by-side arrangement and triangular arrangement) and cluster densities (one, two, three, four, and five plants). The variables measured included soil moisture content, seedling emergence, biomass accumulation and allocation, and grain yield. We have concluded that the performance of inter-row subsoiling treatment was much better than that of no subsoiling treatment. Meanwhile, the triangular arrangement and two plants per cluster were the best choices for soybean biomass accumulation and grain yield in northern China. This study provided a reference for the innovative design of the twin-row planter with subsoiling and the optimisation of soybean agronomy.

Seeding depth is a key indicator for the evaluation of seeding quality, and has a significant impact on crop emergence and yield. However, accurate measurement of the seeding depth in a closed trench during the seeding process remains a challenge in the field of precision corn seeding. To solve these problems, this study presents a depth detection system based on IGWO-BP, in which a multi-sensor data fusion methodology is employed. Two filtering methods, particle filtering and Savitzky-Golay (SG) filtering, were compared for noise reduction in the force sensor data. The results show that SG filtering achieved the best noise reduction performance. The performance of seeding depth prediction models constructed based on the GWO-BP algorithm and the IGWO-BP algorithm was compared and analysed, and the results indicated that the IGWO-BP model achieved the best performance, with R2 = 0.91, MAE = 0.03 cm, and RMSE = 0.08 cm. Further evaluation of the developed prediction model was conducted through field experiments, which revealed that IGWO-BP could accurately predict the seeding depth in closed trenches, with values of R2 = 0.90, MAE = 0.03 cm, and RMSE = 0.08 cm. The proposed system integrated data from a speed sensor, force sensor, and inertial measurement units, and nine input parameters were considered in the prediction model. The dataset was divided into training and testing sets for model validation. The advances described in this study will be of great significance in improving the quality of precision seeding operations.