The problem of bacterial resistance has become more and more common with improvements in health care. Worryingly, the misuse of antibiotics leads to an increase in bacterial multidrug resistance and the development of new antibiotics has virtually stalled. These challenges have prompted the need to combat bacterial infections with the use of radically different approaches. Taking lessons from the exciting properties of micro-/nano-natural-patterned surfaces, which can destroy cellular integrity, the construction of artificial surfaces to mimic natural functions provides new opportunities for the innovation and development of biomedicine. Due to the diversity of natural surfaces, functional surfaces inspired by natural surfaces have a wide range of applications in healthcare. Nature-inspired surface structures have emerged as an effective and durable strategy to prevent bacterial infection, opening a new way to alleviate the problem of bacterial drug resistance. The present situation of bactericidal and antifouling surfaces with natural and biomimetic micro-/nano-structures is briefly reviewed. In addition, these innovative nature-inspired methods are used to manufacture a variety of artificial surfaces to achieve extraordinary antibacterial properties. In particular, the physical antibacterial effect of nature-inspired surfaces and the functional mechanisms of chemical groups, small molecules, and ions are discussed, as well as the wide current and future applications of artificial biomimetic micro-/nano-surfaces. Current challenges and future development directions are also discussed at the end. In the future, controlling the use of micro-/nano-structures and their subsequent functions will lead to biomimetic surfaces offering great potential applications in biomedicine.

The mechanical harvesting of corn has always been a problem for the development of the corn industry. In the present investigation, a tangential flow–transverse axial flow threshing test system was designed based on the 4YL-4/5 harvester. The structure design was modular, and the threshing drum and other key parts could be changed, or the technical parameters could be adjusted according to the needs. Thus, the system becomes suitable to carry out the threshing test of various grains. In this paper, two kinds of systems, a cylindrical plate-tooth mixed row threshing drum and a full cylindrical threshing drum, were designed. Using the same materials and technical conditions, a comparative experiment of the corn grain harvest was carried out to explore the mechanical–technical conditions and methods to reduce the grain breakage rate of corn’s direct harvest.The results showed that the threshing ability and adaptability of the cylinder with a plate-tooth mixed arrangement structure were higher than those of the full cylinder arrangement structure. It was also found that for a higher moisture content (above 28%) of the maize ear, the grain breakage rate met the national standard. On the other hand, the cylinder with a plate-tooth threshing drum can support a wider range of moisture contents and drum peripheral velocities than the full cylinder threshing drum. Within the range of all tested moisture contents and drum peripheral velocities, the minimum grain breakage rate of the full cylinder tooth drum was 3.7%, and the minimum grain breakage rate of the cylinder with the plate-tooth threshing drum was 1.5%, which means a reduction of 59.5% of the breakage rate was achieved.

Aiming at the problem of grain breakage during the harvesting of fresh corn, this paper theoretically analyzes the collision process between the ear picking device and the corn ear, and a flexible ear picking structure composed of flexible materials and buffer springs is determined. Combined with a new harvesting method that reverses the growth direction from top to bottom, a reverse flexible ear plucking device for fresh corn was designed. We used the ADAMS software to simulate the ear picking process of fresh corn, analyze the contact force between the rigid structure and flexible buffer structure under different picking claw speeds and stalk feeding speeds, and obtain the optimal parameter combination: the picking claw speed was 2 m/s, and the stalk feeding speed was 1 m/s. On the basis of the simulation, a reverse flexible fresh corn harvesting bench was built, and the optimal operating parameters were obtained from the test: the speed of ear picking claws was 2.11 m/s; the number of ear picking claws was four; the thickness of the flexible body was 4.52 mm; the stem feeding speed was 1.04 m/s; the corresponding grain breakage rate was 0.128%, which was far lower than the national standard (0.5%); and the ear impurity content was 0.3%, which was far lower than the national standard (2%). The results are consistent with the simulation results, proving that the model is reliable. This research achieved the harvest of fresh corn ears with a low grain damage rate, verified the possibility of reverse flexible ear picking, and provided a reference for the research and development of low-damage fresh corn harvesting machines.

In order to realize mechanized and efficient harvesting of tiger nuts, study the efficient screening technology of beans and soil in a mechanized harvesting operation and improve the harvesting operation efficiency of crawler-type tiger nut harvesters, a theoretical analysis of the motion process of detritus particles on a sieve surface was conducted to determine the main factors affecting the motion of the particles on the sieve surface. A numerical simulation of the sieving process using the discrete element method was conducted to improve the screening efficiency of tiger nuts. The transport law of the debris particle population was analyzed from different perspectives, such as the average velocity of particle motion, particle distribution rate, screening efficiency and loss rate. The effects of factors such as screen amplitude (SA), vibration frequency (VF) and inclination angle (IA) on the sieving performance of the tiger nut threshing and screening device were investigated. The results show that sieving performance evaluation indexes, such as the average speed of particle movement, particle distribution rate, screening efficiency and loss rate, are non-linearly related to the factors of screen amplitude, vibration frequency and screen inclination angle; the effects of amplitude and frequency on the distribution particle size are consistent and show a gradual increase, with the distribution particle size reaching 3.32 mm at an amplitude of 14 mm and 3.46 mm at a frequency of 22 Hz. In the sieving process, the average velocity of the particle population decreases gradually along the direction of motion, and the influence of each factor on the average velocity of the particle population in the motion of the detritus is similar, all showing an increasing trend. This study can provide a reference for exploring the transport law of particles and the efficient screening technology of tiger nuts. Field harvesting tests showed that the screening efficiency and loss rate were 92.87% and 0.83%, respectively, at a screen amplitude of 14 mm, a vibration frequency of 10 Hz and an inclination angle of 2°, and the test results corresponded to the simulation results and met the design requirements of the tiger nut harvester. This study can provide reference for the investigation of the particle transport law and efficient screening technology for tiger nuts.

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.

Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was adopted. Additionally, the feature extraction capability of DenseNet121 was improved by incorporating the attention mechanism of a Convolutional Block Attention Module (CBAM) and a Feature Pyramid Network (FPN) structure. (2) The quality of intact and broken corn kernels and their pixels were found to be coupled, and a linear regression model was established using the least squares method. The results of the test showed that: (1) The MAP^b50 and MAP^m50 of the improved Mask Region-based Convolutional Neural Network (RCNN) model were 97.62% and 98.70%, in comparison to the original Mask Region-based Convolutional Neural Network (RCNN) model, which were improved by 0.34% and 0.37%, respectively; the backbone FLOPs and Params were 3.09 GMac and 9.31 M, and the feature extraction time was 206 ms; compared to the original backbone, these were reduced by 3.87 GMac and 17.32 M, respectively. The training of the obtained prediction weights for the detection of a picture of the corn kernel took 76 ms, so compared to the Mask RCNN model, it was reduced by 375 ms; based on the concept of transfer learning, the improved Mask RCNN model converged twice as quickly with the loss function using pre-training weights than the loss function without pre-training weights during training. (2) The coefficients of determination R² of the two models, when the regression models of the pixels and the quality of intact and broken corn kernels were analyzed, were 0.958 and 0.992, respectively. These findings indicate a strong correlation between the pixel characteristics and the quality of corn kernels. The improved Mask RCNN model was used to segment mask pixels to calculate the corn kernel breakage rate. The verified error between the machine vision and the real breakage rate ranged from −0.72% to 0.65%, and the detection time of the corn kernel breakage rate was only 76 ms, which could meet the requirements for real-time detection. According to the test results, the improved Mask RCNN method had the advantages of a fast detection speed and high accuracy, and can be used as a data basis for adjusting the operation parameters of corn kernel harvesters.