Traditional potato grading in China relies mostly on manual sorting, which is labor-intensive, time-consuming, costly, and inefficient. To enhance the operational performance of potato-grading devices, this paper focuses on optimizing the slide rail structure, which is the key component of a self-developed first-generation potato-grading device. A five-factor, three-level orthogonal experiment was designed, with the experimental factors being the height of the horizontal slide rail, angle of the first-stage inclined slide, angle of the second-stage inclined rail, chain horizontal movement speed, and conveyor belt speed. The indoor experiments were conducted using grading accuracy and grading efficiency as the experimental indicators. On the basis of the analysis of the orthogonal experiment results, two relatively optimal solutions were obtained, and validation experiments were conducted. The validation results show that when the height of the horizontal slide rail was 185 mm, the angle of the first-stage inclined rail was 4°, the angle of the second-stage inclined rail was 2.5°, the horizontal movement speed of the chain was 700 mm/s, and the movement speed of the conveyor belt was 275.60 mm/s, the performance of the movable rotating plate (MRP)-type grading device for potatoes reached its optimum. At this point, the grading accuracy was 94.88%, and the grading efficiency was 13.9477 t/h. Compared with the first-generation grading device, the optimized grading device achieved an improvement of 3.84% in grading accuracy and 12.94% in grading efficiency. The research methodology provided in this paper serves as a reference for the performance optimization of potato-grading devices.

To study the influence law of the overburden rotating plate mechanism on the fragmentation and separation of potato soil agglomerates, a single-factor test and a response surface test simulation analysis of the soil fragmentation process were conducted in EDEM 2022 software. The results of the single-factor test show that the triangular rack blade of the overburdened rotating plate mechanism has the best effect on soil fragmentation and separation. With the increase in the lower blade speed, the upper lift chain bar line speed, and the tilt angle of the mechanism, the effect on the fragmentation and separation of potato soil agglomerates decreases. The response surface test results show that the debris removal rate decreases with the increase in blade speed and tilt angle, the percentage of bond breakage between potato soil particles declines with the rise of blade speed and lift chain bar line speed, and the percentage of bond breakage between soil particles increases with the increase in blade speed and lift chain bar line speed. The optimal solution was obtained by using the optimization function in Design-Expert 13 software, which was adjusted as follows: the blade rack type was triangular, the lift chain bar line speed was 0.307 m/s, the blade speed was 0.4 m/s, and the tilt angle was 40°. The research methods and results provide a reference for the simulation of potato soil crushing and separation motion in a sandy loam soil cultivation area.

The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.

Potatoes are susceptible to skin damage during harvest and transportation, which impacts storage capabilities, increases the risk of microbial contamination and decay, and diminishes both quality and food safety. Rapid detection of potato peel damage is crucial for the subsequent automatic sorting process. To address this, this paper introduces an enhanced YOLOv11n target detection algorithm for identifying damaged potatoes. The primary enhancements include: integrating the MLCA attention mechanism into the C3k2 module of the Backbone network to combine channel and spatial attention, thereby enhancing the network's ability to detect skin damage features; proposing a Re‐Calibration FPN to refine the Neck component, optimizing multi‐scale feature fusion through bidirectional fusion and an adaptive attention mechanism to accurately define the area of skin damage and improve the effectiveness of multi‐scale feature fusion; replacing the first two convolution layers of the Backbone with the EIEStem module, and combining SobelConv with convolutional branches to strengthen the feature representation of potato peel damage images; optimizing the detection head with AFPN to narrow the semantic gap, enhance feature fusion, and further improve the detection of potato peel damage. The WloU loss function is introduced to enhance the precision of boundary regression and reduce the occurrence of missed and false detections of skin damage. Comparative and ablation experiments demonstrate that the improved algorithm outperforms the benchmark YOLOv11n model by 6.9%, 15%, 7.4%, and 11.8% in terms of mAP@0.5, mAP@0.5:0.95, precision rate (P), and recall rate (R), respectively. Compared to existing mainstream algorithms, the proposed algorithm exhibits superior performance in comprehensive comparisons, fully validating its effectiveness in the task of potato peel damage detection.

To investigate the influencing factors and intrinsic relationships between potato impact force and impact damage during potato soil separation, a testing system for potato impact force was established. The impact force test system is composed of a base, a height adjustment device, a simple separation screen, a soil storage tank, an impact force sensor, and so on. By allowing potatoes to fall freely to simulate the collision process, impact force data are collected, and a high-speed camera is used to locate the impact position and analyze the degree of damage. Through the response surface analysis method, the influencing factors and laws of the impact force and impact damage during the collision process between potatoes and rods under soil and no-soil conditions were studied. The results of the response surface analysis indicate that when the screen inclination is within the range of 14.12° to 14.77°, falling height ranges from 453.83 mm to 500 mm, screen rod spacing falls within 36.50 mm to 40 mm, and the screen rod material is rubber. Potatoes can still be at a relatively low damage level when enduring a large impact force. This study has significant implications for reducing potato impact damage during harvesting, enhancing economic benefits in the potato industry, and advancing the technical level of potato harvesting equipment. In the future, based on the results of this study, further exploration can be made to optimize the design of potato harvesting equipment so as to better reduce the damage to potatoes during harvesting and subsequent processing processes and promote the sustainable development of the potato industry.

Potato is exposed to different types of impact loads during harvest, sorting, and transportation. The large impact force between potato and rod-shaped machinery parts causes the potato damage and brings economic losses for the food industry. To obtain the impact force and its influencing laws between potato and rod, both theoretical analysis and finite element analysis (FEA) were used. By defining the impact force coefficient (K), a mechanical model between the impact peak force ( $F_{max}$), potato mass (m), and drop height (h) was established. The FEA virtual experiments showed that $F_{max}$ was increased linearly with the increase in m, h, and $m\sqrt{h}$. When the potato mass was from 0.2 kg to 0.35 kg and the drop height was from 0.2 m to 0.3 m, the regression equation between $F_{max}$ and $m\sqrt{h}$ had a similar structure to the mechanical model, which verified the theoretical analysis and allowed the assessment of potato impact force. The results of this article can contribute to predicting the impact force of potato and solving other problems of solid-like agricultural materials related to impact.