Potato malformation seriously affects commercial value, and its removal has become one of the core steps in the post-harvest and pre-sales process of potatoes. At present, this work mainly relies on manual visual inspection, which requires a lot of labor and incurs high investment costs. Therefore, precise and efficient automatic detection technology urgently needs to be developed. Due to the efficiency of deep learning based on image information in the field of complex object feature extraction and pattern recognition, this study proposes the use of the YOLOv3 algorithm to undertake potato malformation classification. However, the target box regression loss function MSE of this algorithm is prone to small errors being ignored, and the model code is relatively large, which limits its performance due to the high demand for computing hardware performance and storage space. Accordingly, in this study, CIOU loss is introduced to replace MSE, and thus the shortcoming of the inconsistent optimization direction of the original algorithm’s loss function is overcome, which also significantly reduces the storage space and computational complexity of the network model. Furthermore, deep separable convolution is used instead of traditional convolution. Deep separable convolution first convolves each channel, and then combines different channels point by point. With the introduction of an inverted residual structure and the use of the h-swish activation function, deep separable convolution based on the MobileNetv3 structure can learn more comprehensive feature representations, which can significantly reduce the computational load of the model while improving its accuracy. The test results showed that the model capacity was reduced by 66%, mAP was increased by 4.68%, and training time was shortened by 6.1 h. Specifically, the correctness rates of malformation recognition induced by local protrusion, local depression, proportional imbalance, and mechanical injury within the test set range were 94.13%, 91.00%, 95.52%, and 91.79%, respectively. Misjudgment mainly stemmed from the limitation of training samples and the original accuracy of the human judgment in type labeling. This study lays a solid foundation for the final establishment of an intelligent recognition and classification picking system for malformed potatoes in the next step.

Realization of an integrated seeding and compensating potato planter based on one-way clutch

The coefficient of static friction (SF), the coefficient of rolling friction (RF) for particles are two key parameters affecting the repose angle formation and flow characteristics. In this paper, the interaction effects of SF and RF on the formation process of corn repose angle was investigated by the discrete element method. Firstly, five shape kinds of corn models (horse tooth, spherical cone, spheroid, oblate, and irregular shape) were established. Secondly, aluminum cylinder and organic glass box were used to conduct the simulation experiments with taking SF and RF as independent factors and seeing the repose angle as dependent value. Based on simulation results the regression equations were established. Simulation results showed the relation between two factors and the rotational kinetic energy is not nonlinear, and SF does not significantly restrict the flow of corn models after increasing the flow direction, and the effect of SF on the contact number between corns and the bottom plate is remarkable, while the effect of RF on the contact number is not remarkable. Finally, the interaction effect of two factors on the repose angle was analyzed by variance analysis and results showed SF and RF all have a significant impact on the repose angle. Moreover, their interaction effect has an impact on the repose angle. Keywords: corn kernels, coefficient of static friction, coefficient of rolling friction, repose angle, interaction influence DOI: 10.25165/j.ijabe.20211405.4357 Citation: Shi L R, Yang X P, Zhao W Y, Sun W, Wang G P, Sun B G. Investigation of interaction effect between static and rolling friction of corn kernels on repose formation by DEM. Int J Agric & Biol Eng, 2021; 14(5): 238–246.

Potato seed-metering monitoring and improved miss-seeding catching-up compensation control system using spatial capacitance sensor

In this study, we analyzed the effects of three pretreatment methods—microwave, steam, and blanching—on the quality of Honeysuckle to determine the optimal pretreatment method; we then investigated the influence of different drying temperatures, vacuum levels, and rotation speeds on the drying characteristics, color, and active ingredient content of the Honeysuckle that was pretreated by the optimal pretreatment method during rotary microwave vacuum drying. The results indicated that a microwave pretreatment for 75 s was the optimal pretreatment method, which enhanced the retention of active ingredients and effectively improved the browning of the material. During the process of rotary microwave vacuum drying, as the temperature increased, the vacuum level rose, and the rotation speed increased, the drying rate gradually increased. However, excessively high vacuum levels and rapid rotation speeds could actually decrease the drying rate. In addition, the total phenols, total flavonoids, antioxidant activity, and various active ingredients of Honeysuckle dried by rotary microwave vacuum were effectively preserved. Furthermore, its rehydration properties and color were significantly superior to those dried through sun drying. The TIOPSIS method analysis showed that the optimal process parameters were a temperature of 50 °C, a vacuum level of −0.070 MPa, and a rotation speed of 35 Hz, which exhibited the highest relative closeness (0.76). The comprehensive analysis indicated that microwave pretreatment followed by rotary microwave vacuum drying was a promising drying method with potential applications in the dehydration of agricultural products and medicinal plants.

The irregular shape, varying internal compositions, and uneven distributions of maize seeds can degrade the metering performance of maize seeders. This study investigates the effect of irregular maize on the performance of a high-filling-rate seed metering device using DEM. The results show that the seed population in the seed box formed large and small cycles of seed transport due to the disturbances caused by the three wheels. The increasing angular speed of the three wheels reduced the rate of seed filling in the seed filling regions by gravity and disturbances. In addition, an analysis of the repose angle formed by the dropped seed indicated that the angular speed of the taking seed wheel has a certain effect. The large angular speed reduced the coefficient of variation of the seed drop location. The repose angle of the horse tooth seed was greater than those of the other. Meanwhile, the irregular maize seed can also reduce the coefficient of variation of the seed drop location. A comparison between the simulation and experiment results indicates the number of metering seed were basically the same. Keywords: maize seeds, seed metering performance, DEM, verification DOI: 10.25165/j.ijabe.20231601.6813 Citation: Shi L R, Zhao W Y, Sun W, Yang X P, Wang G P, Xin S L. Analysis of the metering performance for typical shape maize seeds using DEM. Int J Agric & Biol Eng, 2023; 16(1): 26–35.