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Tandem aromatic rearrangements represent a potent strategy for modulating the regioselectivity of a rearrangement process. In this article, we disclose two novel tandem aromatic rearrangements triggered by sulfonium [5,5]-rearrangement recently developed in our laboratory. Specifically, the [5,5]-rearrangement of aryl sulfoxides with allyl nitriles, followed by [3,3]-Cope rearrangement, forges a seamless tandem [5,5]-/[3,3]-rearrangement cascade, affording ortho -functionalized aryl sulfides. The other tandem process involves [5,5]-rearrangement of aryl sulfoxides with allyl nitriles, followed by nucleophilic addition/DDQ-oxidation-induced [3,3]-Cope rearrangement, eventually yielding meta -functionalized aryl sulfides. Both consecutive rearrangements enrich the repertoire of tandem aromatic rearrangement methodologies. [ABSTRACT FROM AUTHOR]

Because of its superior strength, low elastic modulus, and exceptional resistance to corrosion, titanium alloy is commonly used as a replacement for carbon steel in the construction of oil well pipes. This paper starts with the application of titanium alloy in oil well pipes in the petroleum industry, summarizes the research progress of its mechanical properties and corrosion properties in titanium alloy drill pipes and oil casing, and compares the fatigue life of several common carbon steel materials and titanium alloy in the petroleum industry. At the same time, the influence of adding metal elements and optimizing the manufacturing process on the corrosion resistance of titanium alloy is discussed. Finally, the problems that titanium alloys may face in the actual production and application process are put forward. [ABSTRACT FROM AUTHOR]

Titanium alloy is widely used as oil drill pipe material because of its light weight, high strength, good toughness, corrosion resistance, fatigue resistance, and good process performance. However, due to its low hardness, poor wear resistance, serious oxidation at high temperature (700 °C), and difficulty in lubrication, in oil and gas field exploration and development drilling, especially in deep wells, high displacement wells, horizontal wells, and highly deviated wells, wear and tear are prone to occur. The application and development of titanium alloys are greatly limited. This paper introduces the research status of the common surface modification technologies of titanium alloys, such as laser cladding, magnetron sputtering, plasma spraying, micro arc oxidation, etc. It points out the improvement effect of various modification technologies on the wear resistance and high-temperature oxidation resistance of titanium alloys and discusses the advantages and disadvantages of various modification technologies. A proposed method for enhancing the wear resistance and high-temperature oxidation resistance of titanium alloys was finally introduced, and its potential for future development was investigated. [ABSTRACT FROM AUTHOR]

In response to issues of the low pesticide-utilization rate caused by the traditional constant spraying method, a variable-spraying system for wheat-field weeds was proposed in this study based on real-time segmentation by deep learning. In this study, the weed density within the operational area was obtained by using the improved DeepLab V3+ semantic segmentation mode, and a variable spray-level model based on real-time weed density and speed was constructed by using PWM variable-spraying technology to adjust the spray volume. The lightweight MobileNet V2 network was selected as its backbone network, and the CA attention mechanism was integrated into the feature extraction module. The mean intersection over the union (MIoU) and mean pixel accuracy (MPA) of the improved DeepLab V3+ were 73.34% and 80.76%, respectively, and the segmentation time for a single image was 0.09 s. The results of field verification tests showed that (1) compared with constant spraying, variable spraying can increase droplet density and save the amount of pesticides, with the droplet density increased by 38.87 droplets/cm2 and the pesticide consumption saved by 46.3%; and (2) at the same speed, the average droplet-coverage rate in the areas with sparse weed density decreased by 13.98% compared with the areas with dense weeds. Under the same plant density, the average coverage rate of 0.5 m/s increased by 2.91% and 6.59% compared with 1 m/s and 1.5 m/s, respectively. These results further demonstrated that the system can automatically adjust the spray volume based on different travel speeds and weed densities. This research can provide theoretical and reference support for the development of new precision-spray plant-protection machinery for wheat fields. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling method of maize in threshing. The static friction coefficient and rolling resistance coefficient of the maize grain were measured using the slope method. The maize grain stacking angle test was designed using the central composite design response surface test. A regression model was established based on the simulation results to find the best combination. The results suggested that the modeling method proposed in this paper was effective in improving the accuracy of maize grain simulation compared with previous methods. Furthermore, this paper presents a method to verify the feasibility and reliability of the maize grain cob discrete element model using the distribution of grain in the granary and the final removal rate as the verification method. The results of the actually simulated threshing test were analyzed using a Wilcoxon signed-rank test, heat map analysis, and the Spearman's rank correlation coefficient. It was found that the DEM model of maize cob is suitable for simulating the maize threshing process. This can aid in further research on the subject. [ABSTRACT FROM AUTHOR]

Since the medium entropy alloy (MEA) was proposed, it has become one of the key areas of material research. Due to the multiple components in the MEA, it has a variety of combinations, and it usually shows high strength, high plasticity and excellent fracture toughness at low temperature. However, at high temperature, its comprehensive mechanical properties and toughness are significantly reduced, which greatly limits its application in engineering. This paper reviews the mechanical properties of MEAs, and discusses the effects of transformation strengthening, solution strengthening and plastic deformation on the fracture toughness of MEAs at high temperatures. It is found that the MEA has competitive impact fracture toughness at low temperature, but with the increase in temperature, the comprehensive mechanical properties of MEA materials will continue to decline, and the impact fracture toughness value will also reduce. The mechanical properties of high/medium entropy alloy can be improved obviously by introducing carbon, oxygen, nitrogen, boron and other interstitial elements. By introducing alloying elements into MEA systems, additional strengthening effects such as solution strengthening, grain boundary strengthening and precipitation strengthening can be achieved, thus improving the fracture toughness of materials at high temperatures. The gradient twin structure constructed by plastic deformation will not change the stacking fault energy (SFE) of MEAs, which is an effective method to improve the quasi-static and impact toughness of MEA. [ABSTRACT FROM AUTHOR]

As greenhouse gas emissions from dairy farms are on the rise, effective monitoring of these emissions has emerged as a crucial tool for assessing their environmental impacts and promoting sustainable development. Most of the existing studies on GHGs from dairy farms involve stationary detections with long response times and high costs. In this study, a greenhouse gas detection system was constructed based on NDIR technology using a single broadband light source and a four-channel thermopile detector for the detection of CH4, N2O, and CO2; the detection range of CH4 was 0~100 ppm; that of N2O was 0~500 ppm; and that of CO2 was 0~20%. After the concentration calibration, the cross-interference between the gas measurement channels was studied, and the least-squares method was used to correct the interference between the three gases. The experimental results showed that the full-range deviation of the detection device was lower than 0.81%, the repeatability was lower than 0.39%, the stability was lower than 0.61%, and the response time was lower than 10 s. This study also carried out on-site testing in Luoyang Shengsheng Ranch (Luoyang, China), and the results show that the error between this device and the PTM600 portable gas analyzer is within 9.78%, and the dynamic response time of this device is within 16 s, at which point the content of greenhouse gases in dairy farms can be measured quickly and accurately. The objective of this study is to enhance the precision and effectiveness of greenhouse gas (GHG) emissions monitoring from dairy farms, thereby contributing to environmental protection and sustainable development goals. By achieving this, we aim to facilitate societal progress towards a greener and low-carbon future. [ABSTRACT FROM AUTHOR]

With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD. [ABSTRACT FROM AUTHOR]

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training. [ABSTRACT FROM AUTHOR]

Medium-entropy alloys (MEA) have excellent mechanical properties and are extremely suitable for applications such as aerospace. However, almost all high- and medium-entropy alloys decrease in strength and ductility at high temperatures. As one of the alternative materials for turbine blades of aircraft engines, high/medium entropy alloys are inevitably subjected to coupling effects of high temperature and high strain rates during service. The present work focuses on improving the mechanical properties of MEAs and briefly describes the mechanical properties at high temperatures and high strain rates. Meanwhile, it integrates literature regarding the methods, including adding high melting point solutes, nanocrystalline MEA and non-isoatomic MEA, to improve the mechanical properties at high temperatures and high strain rates. Finally, we propose the potential research direction and possible problems medium entropy alloys may face in the future. [ABSTRACT FROM AUTHOR]

Subsoiling practice is an essential tillage practice in modern agriculture. Tillage forces and energy consumption during subsoiling are extremely high, which reduces the economic benefits of subsoiling technology. In this paper, a cicada-inspired biomimetic subsoiling tool (CIST) was designed to reduce the draught force during subsoiling. A soil–tool interaction model was developed using EDEM and validated using lab soil bin tests with sandy loam soil. The validated model was used to optimize the CIST and evaluate its performance by comparing it with a conventional chisel subsoiling tool (CCST) at various working depths (250–350 mm) and speeds (0.5–2.5 ms−1). Results showed that both simulated draught force and soil disturbance behaviors agreed well with those from lab soil bin tests, as indicated by relative errors of <6.1%. Compared with the CCST, the draught forces of the CIST can be reduced by 17.7% at various working depths and speeds; the design of the CIST obviously outperforms some previous biomimetic designs with largest draught force reduction of 7.29–12.8%. Soil surface flatness after subsoiling using the CIST was smoother at various depths than using the CCST. Soil loosening efficiencies of the CIST can be raised by 17.37% at various working speeds. Results from this study implied that the developed cicada-inspired subsoiling tool outperforms the conventional chisel subsoiling tool on aspects of soil disturbance behaviors, draught forces, and soil loosening efficiencies. This study can have implications for designing high-performance subsoiling tools with reduced draught forces and energy requirements, especially for the subsoiling tools working under sandy loam soil. [ABSTRACT FROM AUTHOR]

At present, potato seeders in China generally have poor uniformity of seed rows and high coefficients of variation in plant spacing during seed rows, causing difficulties for subsequent mechanized plant protection and harvesting. Based on the effect of seed discharge to analyze the sowing process, a potato seed discharger with a double-layer seed picking spoon structure was designed. By analyzing the seed discharging mechanism and its operation process, the shape and size structural parameters of the seed picking spoon were determined. Finite element simulation of the seed pickup process and seed carrying process of the seed discharging mechanism was carried out by EDEM software to determine the double-layer seed scoop scheme and the range of factors for subsequent tests. A two-factor test was conducted with seeding line speed and seed drop height as test factors, and plant spacing coefficient of variation and seed potato lateral offset dispersion as test indexes. The test results showed that the double-layer seeding spoon chain seeder reduced the coefficient of variation in plant spacing by 5.8%, and the dispersion in lateral offset by 5.5 mm, compared with the single seeding spoon seeder, when the seeding speed was 0.184 m/s and the height of falling seed was 9 cm. [ABSTRACT FROM AUTHOR]

Agaricus bisporus is widely cultivated worldwide due to its considerable economic benefits. The increasingly prominent contradiction between production and labor shortage necessitates the urgent replacement of human workers with intelligent picking technology and equipment. Therefore, a pressure-stabilized flexible end-effector was designed to achieve the rapid and low-loss picking of Agaricus bisporus. The dimensions of the end-effector were determined by measuring the external parameters of Agaricus bisporus. A mechanics model of the end-effector was constructed to analyze the picking process theoretically, and the pre-experiments identified the key performance factors of the end-effector: the thickness of the flexible membrane, the particle filler material, and the pressure stability. A series of experiments were conducted to investigate the factors mentioned above concerning the performance of the end-effector. The results show that the adsorption effect is best when the thickness of the flexible membrane is 0.9 mm, and the particle diameter is 200 mesh quartz. To control the adsorption force of the end-effector accurately during the picking process, a low-cost adsorption force-adjustment system was designed, and the stability of the system was verified. The experimental results showed that the device improved the stability of the adsorption force during the operation of the picking system by 84.71%. An experiment was conducted on the picking of Agaricus bisporus using the designed end-effector. The success rate of picking with the end-effector was 98.50%, and the picking damage rate was 2.50%. [ABSTRACT FROM AUTHOR]

Maize is one of the important grain crops grown globally, and growth will directly affect its yield and quality, so it is important to monitor maize growth efficiently and non-destructively. To facilitate the use of unmanned aerial vehicles (UAVs) for maize growth monitoring, comprehensive growth indicators for maize monitoring based on multispectral remote sensing imagery were established. First of all, multispectral image data of summer maize canopy were collected at the jointing stage, and meanwhile, leaf area index (LAI), relative chlorophyll content (SPAD), and plant height (VH) were measured. Then, the comprehensive growth monitoring indicators CGMICV and CGMICR for summer maize were constructed by the coefficient of variation method and the CRITIC weighting method. After that, the CGMICV and CGMICR prediction models were established by the partial least-squares (PLSR) and sparrow search optimization kernel extremum learning machine (SSA-KELM) using eight typical vegetation indices selected. Finally, a comparative analysis was performed using ground-truthing data, and the results show: (1) For CGMICV, the R2 and RMSE of the model built by SSA-KELM are 0.865 and 0.040, respectively. Compared to the model built by PLSR, R2 increased by 4.5%, while RMSE decreased by 0.3%. For CGMICR, the R2 and RMSE of the model built by SSA-KELM are 0.885 and 0.056, respectively. Compared to the other model, R2 increased by 4.6%, and RMSE decreased by 2.8%. (2) Compared to the models by single indicator, among the models constructed based on PLSR, the CGMICR model had the highest R2. In the models constructed based on SSA-KELM, the R2 of models by the CGMICR and CGMICV were larger than that of the models by SPAD (R2 = 0.837), while smaller than that of the models by LAI (R2 = 0.906) and models by VH (R2 = 0.902). In summary, the comprehensive growth monitoring indicators prediction model established in this paper is effective and can provide technical support for maize growth monitoring. [ABSTRACT FROM AUTHOR]

In this report, we describe the combination of [5,5]‐rearrangement‐triggered dearomatization recently developed in our laboratory with an intramolecular formal [2+2]‐cycloaddition for constructing an intriguing polysubstituted tricyclo[4.2.1.03,8]nonane derivatives. The synthesis features the use of simple and readily available three distinct starting materials including aryl sulfoxides, allyl nitriles, and certain nucleophiles or dienes which directly enriches the diversity of target molecules. This study provides an entirely new protocol for the synthesis of cyclobutane‐centered tricyclic caged skeleton. [ABSTRACT FROM AUTHOR]

Subsoiling is an essential practice in conservation tillage technology. The amount of disturbed soil at various depths resulting from subsoilers with different parameters has an important effect on soil properties (e.g., bulk density and water infiltration). The information regarding the effects of subsoiling on the characteristics of soil water infiltration is essential for the design of subsoiling tools. In this study, the effects of the wing mounting height (h) (75–155 mm) of the subsoiler on soil disturbance and soil water infiltration were modelled using HYDRUS-2D and validated using field experiments. Results showed that reducing h values resulted in larger soil disturbance area ratios, soil water infiltration rates (f(t)), distances of vertical wetting front movement (DVWs), accumulative infiltrations (AINs), and soil moisture contents at depths of 10–30 cm. The relationships among characteristics of soil water infiltration, h and time (t), were developed. The stable infiltration rates (fs) varied quadratically with h and the corresponding coefficient of determination (R2) was 0.9869. The Horton model is more suitable for describing the relationship between f(t) and t under the tested soil conditions, as compared with the Kostiakov and Philip models. According to the results of soil water content at different depths from the HYDRUS simulations and field experiments, the developed soil water infiltration model had a good accuracy, as indicated by RMSEs of <0.05, R2 values of >0.95, and mean relative errors of <12%. The Above results indicated that increasing the hardpan disturbance by optimizing wing parameters of the subsoiler could improve soil water infiltration characteristics. [ABSTRACT FROM AUTHOR]

Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model's performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the cow head region and the mouth region. The ResNet-50-FPN network is employed to extract the cow mouth features, and the CBAM attention mechanism is incorporated to further improve the algorithm's detection accuracy. Subsequently, the object detection results are combined with optical flow information to eliminate false detections. Finally, an interpolation approach is adopted to design a frame complementary algorithm that corrects the detection frame of the cow mouth region. This interpolation algorithm is employed to rectify the detection frame of the cow's mouth region, addressing the issue of missed detections and enhancing the accuracy of ruminant mouth region detection. To overcome the challenges associated with the inaccurate extraction of small-scale optical flow information and interference between different optical flow information in multi-objective scenes, an enhanced GMFlowNet-based method for multi-objective cow ruminant optical flow analysis is proposed. To mitigate interference from other head movements, the MeanShift clustering method is utilized to compute the velocity magnitude values of each pixel in the vertical direction within the intercepted ruminant mouth region. Furthermore, the mean square difference is calculated, incorporating the concept of range interquartile, to eliminate outliers in the optical flow curve. Finally, a final filter is applied to fit the optical flow curve of the multi-object cow mouth movement, and it is able to identify rumination behavior and calculate chewing times. The efficacy, robustness, and accuracy of the proposed method are evaluated through experiments, with nine videos capturing multi-object cow chewing behavior in different settings. The experimental findings demonstrate that the enhanced Faster R-CNN algorithm achieved an 84.70% accuracy in detecting the ruminant mouth region, representing an improvement of 11.80 percentage points over the results obtained using the Faster R-CNN detection approach. Additionally, the enhanced GMFlowNet algorithm accurately identifies the ruminant behavior of all multi-objective cows, with a 97.30% accuracy in calculating the number of ruminant chewing instances, surpassing the accuracy of the FlowNet2.0 algorithm by 3.97 percentage points. This study provides technical support for intelligent monitoring and analysis of rumination behavior of dairy cows in group breeding. [ABSTRACT FROM AUTHOR]

As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus. [ABSTRACT FROM AUTHOR]

This study conducted pin disc friction and wear performance tests on polyethylene-lined oil pipes and four types of centralizing materials (45# steel, nylon, polytetrafluoroethylene (PTFE), and surface alloy coating) in oil fields. The friction coefficient and wear rate were tested, and the wear mechanism was analyzed using scanning electron microscopy (SEM) and three-dimensional confocal microscopy. Using a combination of experimental testing analysis and theoretical research, a comprehensive evaluation of the current wellbore centering and anti-wear technology for oil was conducted. The experimental results indicate that the usage limit of polyethylene-lined oil pipes is 400 N, and compared to metal oil pipe materials, the wear rate of both stabilizing material and tubing material is lower, indicating that it has a certain service life. From the perspective of testing load, taking into account the factors of friction coefficient and wear rate, the recommended sequence of straightening material for polyethylene lined oil pipes is (1) surface alloy coating, (2) nylon, (3) PTFE, and (4) 45# steel. [ABSTRACT FROM AUTHOR]

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the pulping components and the paddy soil during the paddy field pulping process. The findings are valuable in optimizing the parameters of the paddy beating device to improve its working quality and efficiency. However, the lack of accurate soil models for paddy soil has limited the application and development of the discrete element method in paddy pulping research. This study selected the Hertz-Mindlin with Johnson-Kendall-Roberts discrete element model for the pre-pulping paddy loam soil and used the slump error as the test index to select nine parameters, including soil Poisson's ratio and surface energy, as test factors to calibrate the model parameters. The Plackett-Burman test identified soil shear modulus, surface energy, and soil-iron plate static friction coefficient as significant factors affecting the test index. The steepest ascent test results determined the test range of the above parameters. The Box-Behnken test obtained the regression model between the significant factors and the test index, and the regression model was optimized using the slump error as the target. The optimal combination of parameters was surface energy of 3.257 J/m2, soil shear modulus of 0.709 MPa, and static friction coefficient between soil and iron plate of 0.701. The slump simulation test using this combination of parameters yielded an average slump error of 2.04%. The collective results indicate the accuracy of the calibrated discrete element simulation parameters for paddy loam soil. These parameters can be used for discrete element simulation analysis of the paddy pulping process after paddy field soaking. [ABSTRACT FROM AUTHOR]

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPADCL and ear leaf (EL) SPADEL were established, and a linear fitting analysis was conducted combining the measured SPADCL values and SPADEL values on the ground. The results showed that for SPADCL, the R2 of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPADEL, the R2 of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance and light interference in navigation path recognition in a field environment. This technique is based on the Res2net50 model and incorporates the Squeeze-and-Excitation Networks (SE) attention mechanism to focus on the key aspects of the image. The empty space pyramid pooling module is presented to further extract high-level semantic data and enhance the network's capacity for fine-grained representation. A skip connection is used to combine the high-level semantic characteristics and low-level textural features that are extracted. The results of the ridge prediction are then obtained, followed by the realization of the final image segmentation, through sampling. Lastly, the navigation line is fitted once the navigation feature points have been retrieved using the resulting ridge segmentation mask. The outcomes of the experiment reveal that: the Mean Intersection over Union (MIOU) and F-measure values of the inter-ridge navigation path extraction approach suggested in this paper are increased by 0.157 and 0.061, respectively, compared with the Res2net50 network. Under various illumination situations, the average pixel error is 8.27 pixels and the average angle error is 1.395°. This technique is appropriate for ridge operations and can successfully increase network prediction model accuracy. [ABSTRACT FROM AUTHOR]

This study designed a cam-linked planetary gear system automatic seedling picking mechanism to address unstable operation and issues of high damage to the substrate caused by the picking mechanism of the dryland vegetable seedling transplanter. Through an analysis of the kinematic principle of the structure and the establishment of the kinematic model, computer-aided analysis software was developed using Visual Basic 6.0. Consequently, a set of structural parameter values satisfying the target trajectory was derived employing the human–computer interaction method, and the 3D model was designed. The model was imported into Adams for kinematic simulation, and the seedling picking mechanism's trajectory during the operation was obtained through simulation. Modal analysis of the model was performed using Ansys, and the first six-order modal vibration patterns and modal frequencies of the seedling picking mechanism were obtained under the simulated working environment. The results confirmed that no resonance occurred during the operation. Comparisons of the seedling picking needle trajectory with the idle test revealed that the theoretical, simulated, and test trajectories were approximately identical. This proved the reliability of the theoretical design of the seedling picking mechanism, the machining of the parts, and the test bench construction. The success rates of seedling picking were 97.66, 96.09, 93.75, and 90.63% at 90, 100, 110, and 120 plants/min, respectively, with rates of substrate damage of 4.43%, 6.73%, 9.57%, and 14.37%, respectively. Thus, the experimental results confirmed that the operating parameters of the cam-linked planetary gear system seedling picking mechanism satisfied the design requirements. [ABSTRACT FROM AUTHOR]