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.

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.

Garlic root cutting is generally performed manually; it is easy for the workers to sustain hand injuries, and the labor efficiency is low. However, the significant differences between individual garlic bulbs limit the development of an automatic root cutting system. To address this problem, a deep learning model based on transfer learning and a low-cost computer vision module was used to automatically detect garlic bulb position, adjust the root cutter, and cut garlic roots on a garlic root cutting test bed. The proposed object detection model achieved good performance and high detection accuracy, running speed, and detection reliability. The visual image of the output layer channel of the backbone network showed the high-level features extracted by the network vividly, and the differences in learning of different networks clearly. The position differences of the cutting lines predicted by different backbone networks were analyzed through data visualization. The excellent and stable performance indicated that the proposed model had learned the correct features in the data of different brightness. Finally, the root cutting system was verified experimentally. The results of three experiments with 100 garlic bulbs each indicated that the mean qualified value of the system was 96%. Therefore, the proposed deep learning system can be applied in garlic root cutting which belongs to food primary processing.

In view of the poor effectiveness of existing potato cleaning methods in China and reflecting the findings of a research analysis of basic sizes and types of impurities on potato tubers, a gradient cleaning method for potato based on a multi-step dry-cleaning and wet cleaning operation was proposed. The method mainly consists of dry-cleaning and wet cleaning. The dry-cleaning stage, which combines vibration and brushing, could effectively remove impurities such as residual rhizomes, peeled potato skin, and large pieces of soil and crushed stone from the surface of potato tubers. The wet cleaning stage adopts the gradient cleaning method of pre-cleaning, rough cleaning and fine cleaning, which could further remove soil and crushed stone attached to the surface and hidden in the sprout eyes of potato tubers. The optimal parameter combination for the gradient cleaning method was determined as follows. The potato feeding amount was 3 t/h, the speed of the rubber chain rod mechanism was 25 r/min, the speed of the first and third brush roller was 40 r/min, the speed of the second and fourth brush roller was 56 r/min, the moving speed of the immersion mechanism conveying net chain was 0.04 m/s, the speed of the brush roller in the high pressure spray and brush roller combination mechanism was 40 r/min, the ultrasonic power was 1200 W, the ultrasonic frequency was 33 kHz, the bubble intensity was 300 L/min, and the moving speed of the conveying net chain in the ultrasonic and bubble combination mechanism was 0.05 m/s. Taking the impurity removal rate and damage rate of potato tuber as the test indexes, a potato cleaning performance test was carried out under the optimal parameters combination. The results showed that the average impurity removal rate and damage rate of potato tubers were 99.05% and 2.48%, respectively. Additionally, the operational performance fully met the requirements for potato cleaning. This study provides a new method for potato cleaning in China and can also provide a reference for cleaning other root and tuber crops.

Performance test of a half-feed peanut-cleaning picker suitable for clay hill areas in southern China