The rapid development of peanut mechanization has increased the amount of dust expelled from peanut mechanized operations, which degrades the air quality and endangers the health of agricultural workers. Therefore, the purpose of this study is to figure out the characteristics of dust emission from mechanized peanut harvesting. To this end, the particulate matters of diameters ≤ 2.5 μm and ≤10 μm and the total suspended particles were sampled in real time during peanut harvesting in Henan Province, China, and the airborne particle concentrations and particle size distributions were measured. The dust particles discharged during the mechanized peanut harvesting were concentrated within the 2~30 µm size range. When the wind speed was reduced below the settling velocity of the largest particles, the more massive particles were carried in the downwind. The amount of free silica in the dust samples was determined by X-ray diffraction analysis. Both the total dust and free silica concentrations exceeded the occupational exposure and threshold limits. To improve the characteristics of dust emission, the microstructure and dispersion of the dust were also investigated. Reducing the agricultural operations during periods of high wind speed, low crop-moisture content, and low air humidity is recommended for reducing the dust exposure of workers. The results will provide guidance and technical support for reducing the dust emissions of mechanized harvesting operations, improving air quality, and reducing the health hazards to operators.

This study presents a tangential-axial flow picking device for peanut combine harvesting. The device was designed with reference to the tangential-axial flow threshing mechanism of the grain combine harvesters. The main purpose of this study was to solve the problem of the high rate of loss and damage in the picking operation of the peanut combine harvesters in China. Through the theoretical analysis and design calculation of the key components of the peanut picking device, the structural and working parameters were determined. The orthogonal test was carried out by taking the feeding amount of the peanut plant, the picking clearance, the speed of the tangential cylinder, and the speed of the axial cylinder as the test factors. Additionally, the non-picking loss rate, entrainment loss rate, and damage rate of the peanut pods were used as the test indexes. The test results were analyzed by range analysis and analysis of variance, and the test parameters were optimized by fuzzy comprehensive evaluation method. The optimal parameter combination for the tangential-axial flow picking device was determined as follows. The feeding amount of the peanut plant was 2 kg/s, the picking clearance was 35 mm, the speed of the tangential cylinder was 360 r/min, the speed of the axial cylinder was 425 r/min. At this time, the non-picking loss rate was 0.52%, the entrainment loss rate was 0.54%, and the damage rate was 0.75%. The test results fully met the standard requirements of the peanut picking operation. This research provides a technical basis for the application of the tangential-axial flow picking device in peanut combine harvesters.

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.

Potato stem removal is one of the critical technical problems of potato mechanized harvesting; it directly affects the quality of potato harvesting and potato storage. There have been several studies on potato stem removal mechanisms. In practice, however, it was found that the potato stem removal rate was greatly influenced by the posture of the stem before it entered the removal mechanism. In this study, we designed a potato stem posture adjustment mechanism consisting of elastic curtains. A test rig was built to investigate the effect of curtain height, curtain width, and curtain suspension height on potato passage rate and potato stem removal rate. The Box–Behnken design (BBD), combined with the response surface method, was used to conduct the test. The optimal construction and installation parameters for each elastic curtain were determined as 278.93 mm for the curtain height, 20 mm for the curtain width, and 260 mm for the curtain suspension height. The predicted values of potato passage rate and potato stem removal rate under the optimal parameters were 92.36% and 82.83%, which were consistent with the validation test results. Based on the optimization results, a rigid-flexible coupled simulation model for a potato stem transported-posture adjustment process based on Abaqus and Adams was constructed. The maximum impact of the elastic curtain of the stem posture adjustment mechanism on the potato stem was 15.91 N and caused the stem to spring back. The projection angle β′ of the stem posture angle in the xoz plane before posture adjustment was 19.07°, and the β′ of the stem after posture adjustment was 87.18°. At this time, the stem was basically parallel to the rod of the separating sieve and had a high probability of falling from the gap of the bar to complete the removal of the stem. Overall, the stem transport position adjustment mechanism effectively adjusted the stem transported posture and improved the stem removal rate in potato mechanical harvesting.

Potato stems removal is an important part of mechanized potato harvesting. However, there is still limited research on the physical properties of potato stems, especially the determination of Poisson’s ratio and Young’s modulus. This study determined the Poisson’s ratio and Young’s modulus of the potato main stems at different heights above the ground. Since the Potato stems are viscoelastic cylinders with non-standard circular cross-sections and complex textures, the existing determination methods are difficult to apply. We propose a new method to determine Poisson’s ratio and Young’s modulus by combining image processing in the mechanical compression process. The feasibility of this method was verified by determining the hardness value of 65 Shore ‘A’ nitrile rubber specimens, and the measured Poisson’s ratio and Young’s modulus were close to the relevant literature. This method can be used for the determination of potato stems. Atlantic potatoes are widely grown for their high solids content, resistance to pests and diseases, and good processing quality. Ten Atlantic potato main stems were randomly selected at harvest time. Specimens with a length of 11 ± 1 mm were taken at 0 cm, 10 cm, 20 cm, and 30 cm above the ground from each stem. The average values of the axial Poisson’s ratio were determined as: 0.21, 0.28, 0.30, 0.32, and the axial Young’s modulus as: 15.90 MPa, 12.38 MPa, 11.68 MPa, 11.28 MPa. This study has provided critical basic data for the discrete element model construction of potato stems and numerical simulation of potato haulm killers and potato harvesters, which is beneficial for improving the harvest quality of potato. It also provides new ideas for Poisson’s ratio and Young’s modulus measurement of non-regular cross-sectional cylindrical viscoelastic materials.

In this study, we quantified and analyzed the root-cutting process of garlic with a test bench with pressure sensors on the basis of the comparative analysis of various information perception methods. On the basis of the output value of the pressure sensor, the force curve of garlic roots was plotted, and the double round blade cutting module is optimized on the basis of the force curve diagram. The innovative proposal of slotted round blades for garlic root cutting is presented here. The round blade diameter is 110 mm, the center distance is 100 mm, the blade thickness is 1 mm, and the blade speed is 1200 r/min. According to the analysis of the force curve, it was found that the slotted round blade with the slanted blade could generate a strong thrust to cut the roots. The cutting effect was better and the cutting surface of the roots was straight. The slotted blade meets the need for cutting garlic roots.