Subsoiling has been acknowledged worldwide to break compacted hardpan, improve soil permeability and water storage capacity, and promote topsoil deepening and root growth. However, there exist certain factors which limit the wide in-field application of subsoiling machines. Of these factors, the main two are poor subsoiling quality and high energy consumption, especially the undesired tillage depth obtained in the field with cover crops. Based on the analysis of global adoption and benefits of subsoiling technology, and application status of subsoiling machines, this article reviewed the research methods, technical characteristics, and developing trends in five key aspects, including subsoiling shovel design, anti-drag technologies, technologies of tillage depth detection and control, and research on soil mechanical interaction. Combined with the research progress and application requirements of subsoiling machines across the globe, current problems and technical difficulties were analyzed and summarized. Aiming to solve these problems, improve subsoiling quality, and reduce energy consumption, this article proposed future directions for the development of subsoiling machines, including optimizing the soil model in computer simulation, strengthening research on the subsoiling mechanism and comprehensive effect, developing new tillage depth monitoring and control systems, and improving wear-resisting properties of subsoiling shovels.

Agricultural machinery can improve agricultural productivity and promote agricultural scale operation. However, machinery operations lead to increased dust in farmland and affect the atmospheric environment; thus, they have been increasingly emphasized. In this study, the effects of agricultural machinery operations in wheat cultivation were investigated regarding the emissions of three kinds of particulate matters, namely fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀) and total suspended particulate (TSP), from farmland in Beijing. The results showed that the total dust emission from the traditional tillage mode, including straw crushing, rotary tilling and sowing, was 3.990 g per hectare, which was larger than that of the conservation tillage mode including only no-tillage sowing (0.407 g per hectare). The total dust emission for one hectare of farmland under the two modes was 3.415 g, 0.497 g, 0.407 g and 0.078 g for straw shredding, rotary tillage, no-tillage sowing and conventional sowing, respectively. The values of PM_2.5/PM₁₀ and PM_2.5/TSP decreased in each tillage section after each agricultural machinery operation, while the values of PM₁₀/TSP were basically unchanged, indicating that particulate matter emissions from farmland due to agricultural machinery operations are mainly PM₁₀ and TSP. The dust concentration generated by agricultural machinery increased with an increase in the speed of the machinery operation, provided that the quality of the operation was guaranteed. This study provides guidance for reducing dust emissions from mechanized operations, improving air quality and decreasing health hazards to operators of agricultural machinery.

Cutting is a significant part of agricultural material processing, and the cutting technology determines the quality of agricultural products. Water jet cutting technology is a non-contact and cold cutting technology suitable for cutting agricultural materials. It can realize an environmentally friendly cutting process avoiding such problems as heat generation, sharpening and cleaning blades, and microbial cross-contamination. This paper reviews the current status of water jet cutting of six kinds of agricultural materials, including vegetables, fruits, meats, woods, stems, and soils. By analyzing how to complete different cutting operations, improve cutting ability, or control post-cutting influences, the problems and solutions of water jet cutting of each material are summarized. Then, combined with the application requirements, some suggestions are put forward for developing water jet cutting technology. The results would help researchers determine key information required by cutting agricultural materials and provide a reference for further research on water jet cutting technology in agricultural engineering.

Anti-blocking performance of ultrahigh-pressure waterjet assisted furrow opener for no-till seeder

Straw mulching in conservation tillage farmland can effectively promote land utilization and conservation. However, in this farming mode, surface straw suppresses weed growth, affecting weed size and position distribution and obscuring the weeds, which hampers effective weed management in the field. Accurate weed identification and localization, along with efficient herbicide application, are crucial for achieving precise, efficient, and intelligent precision agriculture. To address these challenges, this study proposes a weed detection model for a targeted spraying system. Firstly, we collected the dataset of weeds in a straw-covered environment. Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. Thirdly, we designed a targeted spraying system by combining the proposed model with the targeted spraying device. Through model test and spraying experiments, the results demonstrated that while the model exhibited a 0.9% decrease in average detection accuracy for weeds, it achieved an 8.46% increase in detection speed, with model memory and computational load reduced by 50.36% and 53.16%, respectively. In the spraying experiments, the proposed method achieved a weed identification accuracy of 90%, a target localization error within 4%, an effective spraying rate of 96.3%, a missed spraying rate of 13.3%, and an erroneous spraying rate of 3.7%. These results confirm the robustness of the model and the feasibility of the targeted spraying method. This approach also promotes the application of deep learning algorithms in precision weed management within directional spraying systems.

In view of the difficulties in weeding and plant protection in the middle and late period of maize planting, this paper proposed a self-propelled thermal fogger chassis. According to the theoretical calculation and agronomic requirements for maize planting, the structure and working principles of the self-propelled thermal fogger chassis were introduced. On this basis, the multi-body dynamics model of chassis structure was established, and the chassis traction, steering and obstacle surmounting performances were also analyzed. Then the rationality and the feasibility of the design were verified through the furrow running test and test equipped with thermal fogger. Test results showed that, the traction performance improves with the decrease of soil deformation index and increase of cohesion, and when track pre-tensioning force was about 1000 N, the machine had a good traction performance; with the decrease of the soil deformation index and the increase of cohesive force, the stability of the single side brake turn of the chassis becomes better; on the contrary, with the increase of the tightness of the crawler, the steering radius turns smaller and the steering stability becomes worse. Under heavy clay, with the pre-tensioning of 1000 N, the machine has better steering stability and smaller turning radius. The obstacle-surmounting simulation result shows that on sandy soil road, the maximum climbing angle for the chassis is 42°, the height of vertical obstacle crossing is 170 mm and the trench width is 440 mm. The study provides a reference for the design of plant protection machinery in the middle and late stages of maize planting. Keywords: maize inter-rows, intelligent chassis, traction performance, steering performance, obstacle surmounting performance DOI: 10.25165/j.ijabe.20181105.3607 Citation: Chen L Q, Wang P P, Zhang P, Zheng Q, He J, Wang Q J. Performance analysis and test of a maize inter-row self-propelled thermal fogger chassis. Int J Agric & Biol Eng, 2018; 11(5): 100–107.