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.

Poor shelling quality degrades the performance and profit of the peanut industry. The Hertz theory and the wear resistance experimental method were applied to identify a highly wear-resistant material guaranteeing a low mechanical damage rate (MDR) of peanut kernels. The Box–Behnken design method was applied in the experiment to illustrate the influence of the material’s elastic modulus (X₁), radius of curvature of the key parts (X₂), rotating speed of the shelling drum (X₃), and clearance between the shelling drum and concave screen (X₄) on MDR and shelling efficiency (SE). Depending on the analysis of variance, the weights of the influential factors were observed as X₁ > X₃ > X₄ > X₂. The mathematical models of MDR and SE were derived from the least squares’ method, and four-dimensional slice diagrams of the three most significant factors were used to illustrate the trends of MDR and SE. A multi-objective analysis provided the optimal combination of parameters as: X₁ = 10 MPa, X₂ = 12.77 mm, X₃ = 277.48 r/min, and X₄ = 24.24 mm, yielding MDR = 4.89% and SE = 97.91%. The results were further verified by a production trial test, proving that the proposed solution with the selected material, machine design, and working parameters were effective in improving peanut shelling quality.

To fill the research gap in the mechanical and physical properties of different varieties of sweet potatoes at different points in the harvest period and to provide a theoretical basis for the design of key components of the sweet potato harvester, the physical properties of Su-Shu 16, Su-Shu 36, and Ning-Zi 4 during the harvest period were studied at three time points: 15 October, 25 October, and 4 November 2023. The moisture content of sweet potatoes was determined using the DGF30/7-IA electric hot air-drying oven. The results showed that the moisture content of sweet potatoes decreased with increasing growth time at three different time points during the harvest period. The moisture content of Su-Shu 16 was, on average, 12.74% higher than that of Su-Shu 36, while the moisture content of Ning-Zi 4 was, on average, 8.07% higher than that of Su-Shu 36. The density of Su-Shu 36 measured by the drainage method is greater than that of Su-Shu 16 and Ning-Zi 4, but the difference is relatively small, and the density tends to decrease slowly with the increase of growth time. Using an electronic universal testing machine, compression tests were conducted on Su-Shu 16, Su-Shu 36, and Ning-Zi 4 at loading speeds of 5 mm/min and 10 mm/min, respectively. The results showed that the compressive strength limit range of Su-Shu 36 was slightly higher than that of Su-Shu 16 and significantly higher than that of Ning-Zi 4. The Poisson’s ratio, elastic modulus, and shear modulus values of Su-Shu 16 and Su-Shu 36 were similar and much higher than those of Ning-Zi 4. Studying sweet potatoes’ growth and physical characteristics for different purposes can provide data references for the design of digging depth, working width, and conveyor chain gap of sweet potato harvesters, as well as data references for sweet potato simulation experiments.

The digging depth is an important factor affecting the mechanized garlic harvesting quality. At present, the digging depth of the garlic combine harvester (GCH) is adjusted manually, which leads to disadvantages such as slow response, poor accuracy, and being very dependent on the operator’s experience. To solve this problem, this paper proposes a machine vision-based automatic digging depth control system for the original garlic digging device. The system uses the improved YOLOv5 algorithm to calculate the length of the garlic root at the front end of the clamping conveyor chain in real-time, and the calculation result is sent back to the system as feedback. Then, the STM32 microcontroller is used to control the digging depth by expanding and contracting the electric putter of the garlic digging device. The experimental results of the presented control system show that the detection time of the system is 30.4 ms, the average accuracy of detection is 99.1%, and the space occupied by the model deployment is 11.4 MB, which suits the design of the real-time detection of the system. Moreover, the length of the excavated garlic roots is shorter than that of the system before modification, which represents a lower energy consumption of the system and a lower rate of impurities in harvesting, and the modified system is automatically controlled, reducing the operator’s workload.

With the advancement of technology and the national effort to promote the development of agricultural mechanization, sweet potato harvester technology is continuously evolving. To solve the problems related to high skin breakage rates and high damage rates during sweet potato harvesting, it is necessary to develop a conveying and separation mechanism that facilitates low skin breakage rates, low injury rates, good potato–soil separation, and smooth transport. This paper elaborates on the working modes of mechanized sweet potato harvesting, focusing on the current state of research on the conveying and separating mechanisms of sweet potato harvesters both in China and elsewhere. The functions of different types of conveying chains are summarized, the conveying and separating technologies used for the mechanized harvesting of other root crops that can be applied to sweet potato harvesters are analyzed, and an outlook on the development trends regarding sweet potato harvesters is provided.

The quality of walnuts deteriorates owing to the poor quality of the shelling equipment. The improvement of shelling quality is urgently required for walnut processing. In this study, systematic research was carried out on the changes in walnut mechanical properties, mechanical model of walnut shelling, and the key parameters of the equipment. The key parameters were determined as the angles of the shelling conic roller (X₁), speeds of the shelling conic roller (X₂), clearance between the shelling conic roller and the static roller (X₃), and the moisture content of walnuts (X₄). The Box–Behnken design method was used for the experimental design, an analysis of variance was applied to determine the graded significance of each variable on the rate of high-quality kernel (RHQK) and rate of shell rushing (RSC), and the multi-objective optimization method was used to obtain the optimal parameters for RHQK and RSC. The ranking of factors affecting RHQK and RSC were: (X₃) > (X₄) > (X₂) > (X₁) for RHQK, and (X₂) > (X₃) > (X₄) > (X₁) for RSC. The ranks of significant interactive effects among the factors were as follows: (X₁ X₂) > (X₂ X₃) for RHQK and (X₂ X₃) > (X₃ X₄) > (X₂ X₄) for RSC. The multi-objective optimization results showed that the optimal combination was $X_1$ = 15.83°, $X_2$ = 17.93 rpm, $X_3$ = 45 mm, and $X_4$ = 9.5%, yielding RHQK = 84.54%, and RSC = 99.15%. The verification test of the optimal results further illustrates the accuracy of the optimization. The obtained results showed that the quality of walnut shelling can be improved by adjusting the moisture content of walnuts and optimizing key parameters of the equipment. This method also represents a potential solution for improving the shelling quality of other nuts.