The drying process is crucial for maintaining the quality of peanut pods and mitigating the risk of mold proliferation. The aim of this study was to investigate the kinetic characteristics of a synergistic intermittent microwave and a hot-air-drying technique, with an emphasis on enhancing efficiency and reducing energy consumption. A comprehensive analysis was performed on key parameters—including moisture content, moisture ratio, drying rate, and effective moisture diffusion coefficient—to elucidate moisture migration during the drying process. The findings indicated that higher microwave power significantly accelerates the drying rate while substantially shortening the drying time for peanut pods. The effective moisture diffusion coefficient was found to range from 0.987 × 10⁻⁹ to 1.227 × 10⁻⁹ m²/s, with the Verma model demonstrating superior accuracy in predicting drying behavior. The experiment achieved a minimum specific energy consumption of 12,535 kJ/kg and a maximum thermal efficiency of 18.1% at a microwave power density of 0.8 W/g combined with a temperature of 40 °C. However, the observed thermal efficiency was lower than that in previous studies, mainly due to the suboptimal regulation of experimental parameters. Future research should focus on optimizing these parameters and further exploring the impact of this drying method on energy consumption to achieve more efficient and sustainable peanut drying.

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.

In order to solve problems such as poor applicability of headers, weak separation ability of threshing mechanisms and poor impurity-removal ability of cleaning devices in the existing seed harvest methods of Chinese milk vetch (Astragalus sinicus L.), a combined Chinese milk vetch seed harvester was designed in this paper. The parameters of the key components, such as the flexible anti pod-dropping seedling-lifting header, the longitudinal rod-teeth-type threshing device and the air-sieve-type layered impurity-controlled cleaning device, were designed and optimized. Aiming at reducing seed loss rate, breakage rate and impurity rate of Chinese milk vetch during the mechanical harvesting process, through multi-parameter optimization, the best combination of working parameters was obtained: machine forward speed was 3 km·h⁻¹, rotation speed of the threshing drum was 550 r·min⁻¹, rotation speed of the cleaning fan was 990 r·min⁻¹ and the scale sieve’s opening was 35 mm. Field tests were performed under these parameters, and the results showed that the seed loss rate of Chinese milk vetch was 2.35%, the breakage rate was 0.22% and the impurity rate was 0.51%, which were better than the technical requirements of loss rate and breakage rate less than 5% and impurity rate less than 3% specified in relevant standards. The research results can solve the shortage problem of efficient seed harvest equipment in large-scale planting areas of Chinese milk vetch, and will further help to carry out seed harvest experiments on different varieties of Chinese milk vetch and other green manure varieties in paddy fields.

In order to enable accurate and efficient rice quality evaluation through full-surface defect detection of brown rice, a detection system based on convolutional neural network (CNN) was developed. A dataset of images of five categories—unhulled, normal, broken, cracked, and insect-bitten brown rice—was collected. Three CNN models, YOLOv5s, YOLOv7, and Faster R-CNN, were evaluated and compared with traditional algorithms including support vector machine (SVM) and back propagation (BP) neural networks. Experimental results showed that CNN-based methods in the present database significantly outperformed traditional approaches, with the YOLOv5s model achieving the best comprehensive performance: 95.80% detection accuracy, 10.90 ms inference time per image, and 92 frames/s processing speed. An improved Rice-YOLOv5s algorithm was further proposed and validated through batch detection experiments, achieving an average recognition accuracy of 96.44% and a processing time of 9.2 ms per image, which is equivalent to approximately 108.7 FPS. This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application.