As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.

The growth of field crops needs appropriate soil nutrients. As a basic fertilizer, liquid manure provides biological nutrients for crop growth and increases the content of organic matter in crops. However, improper spraying not only reduces soil fertility but also destroys soil structure. Therefore, the precise control of the amount of liquid manure is of great significance for agricultural production and weight loss. In this study, we first built the model of spraying control, then optimized the BP neural network algorithm through a genetic algorithm. The stability and efficiency of the optimized controller were compared with PID, fuzzy PID and BPNN-PID control. The simulation results show that the optimized algorithm has the shortest response time and lowest relative error. Finally, platform experiments were designed to verify the four control algorithms at four different vehicle speeds. The results show that, compared with other control algorithms, the control algorithm described here has good stability, short response time, small overshoot, and can achieve an accurate fertilizer application effect, providing an optimization scheme for research on the precise application of liquid manure.

The compression molding performance of a substrate block has a significant effect on the quality and stability of mechanical transplanting. The physical experiment and DEM simulation were combined to evaluate the compression molding behavior of substrate block in this study. A calibration procedure of DEM parameters of peat particles was proposed at first. Then, the above parameters were brought into the contact model of the compression system–particles, and the effect of the loading speed on the compression behavior of the peat substrate block was investigated. The compressive force–displacement curves of the simulated and measured tests were all contained in the initial linear stage and non-linear stiffing stage. The particle number of central sections was higher than side section, and the variable coefficient was greater at higher loading speed. The substrate blocks all expanded after demolding. The higher the loading speed, the greater the expansion in the height’s direction, and the easier it was for cracks to be generated near the bottom. This study will provide a reference for the design of substrate block forming machines.

Aiming to solve the problems of easy clogging and high energy consumption of multi-way fertilization devices for liquid manure, a crushing and impurity removal device for liquid manure was designed by combining the physical characteristics of liquid manure and impurities, and building the corresponding test bench. The proposed device could crush flexible impurities such as straw and filoplume and intercept hard impurities with high density. The main structural parameters of the device were determined according to the survey analysis and the theoretical design. The influences of cutter head shape, cutter edge angle, cutter shaft speed, and cutting clearance on the disqualification rate and energy consumption of straw crushing were obtained by a single-factor experiment. Furthermore, the Box–Behnken central composite design method of the response surface was employed to investigate the effects of the cutter shaft speed, cutting clearance, and cutter edge angle on the disqualification rate and energy consumption of straw crushing. In addition, the working parameters of the device were optimized by employing the response surface method. On this basis, the mathematical relationship model among the disqualification rate, energy consumption, and all influencing factors was established. The results show that the optimal combination of working parameters includes a cutter shaft speed of 312 r/min, a cutting clearance of 1.4 mm, and a cutter edge angle of 45°. From the prediction model, the predicted failure rate was 4.15%, and the predicted energy consumption was 47.53 J. The verification experiment was then performed under the optimal combination of working parameters. The obtained disqualification rate was 4.08% and the energy consumption was 47.56 J, which met the design and work requirements.

Due to the lack of effective numerical simulation and the absence of an accurate and reliable bonded discrete element model for tobacco stalks during mechanised harvesting, the design and optimisation of tobacco stalk pulling and harvesting equipment have been largely hindered. In this study, post-harvest stalks of flue-cured tobacco transplanted in single-ridge cultivation were taken as the research object; Zhongchuan 208 stalk samples with an average outer diameter of 36.78 ± 1.05 mm and an average moisture content of 82.5% were selected, their intrinsic and contact parameters were measured, and a mechanical simulation model of the stalks was established based on the Bonding V2 contact model. With the maximum crushing force (F_C) as the response value, Plackett–Burman tests were first used to screen significant factors, the steepest ascent tests were then employed to determine the optimal region of the significant factors, and finally a second-order regression model between the maximum crushing force and the significant parameters was established using central composite design, yielding the optimal parameter combination: normal stiffness per unit area of 5.498 × 10⁹ N·m⁻³, tangential stiffness per unit area of 1.434 × 10⁹ N·m⁻³, critical tangential stress of 9.146 × 10⁶ Pa, and bonding radius of 1.304 mm. Under this parameter combination, compression and bending simulation tests were carried out; five independent repeated simulations gave an average maximum crushing force of 2410.22 N, a standard deviation of 225.39 N, and a coefficient of variation of 9.35%, with a relative error of 4.32% against the physical test mean; the relative errors of the peak displacement and the maximum bending force were 2.62% and 4.74%, respectively. In the physical control tests with and without epidermis, the relative error between the mean maximum bending forces of the two groups was 8.68%. The results indicate that the discrete element model established in this study can effectively characterise the macroscopic peak load-bearing response of tobacco stalks under the tested conditions and can provide data support for the design and parameter optimisation of stalk pulling equipment.