An appropriate drying method is crucial for producing high-quality dried apricots. In this study, the effects of four drying methods, hot air drying (HAD), infrared drying (IRD), pulse vacuum drying (PVD), and vacuum freeze-drying (VFD), on the drying kinetics and physical and nutritional characteristics of apricot slices were evaluated. PVD required the shortest time (16.25 h), followed by IRD (17.54 h), HAD (21.39 h), and VFD (34.64 h). VFD resulted in the best quality of apricot slices, with the smallest color difference (ΔE = 13.64), lowest water activity (0.312 ± 0.015) and browning degree (0.35), highest color saturation (62.84), lowest hardness (8.35 ± 0.47 N) and shrinkage (9.13 ± 0.65%), strongest rehydration ability (3.58 ± 0.11 g/g), a good microstructure, and high nutrient-retention rates (ascorbic acid content: 53.31 ± 0.58 mg/100 g, total phenolic content: 12.64 ± 0.50 mg GAE/g, and carotenoid content: 24.23 ± 0.58 mg/100 g) and antioxidant activity (DPPH: 21.10 ± 0.99 mmol Trolox/g and FRAP: 34.10 ± 0.81 mmol Trolox/g). The quality of PVD-treated apricot slices was second-best, and the quality of HAD-treated apricot slices was the worst. However, the energy consumption required for VFD was relatively high, while that required for PVD was lower. The results of this study provide a scientific basis for the large-scale industrial production of dried apricots.

The presence of microplastics in soil has become a significant global concern, posing a threat to environmental, agricultural, and human health. However, the use of various detection methods has led to significant discrepancies in the statistics reported for the abundance of soil microplastics across the available literature, resulting in poor comparability across the available literature. Studies have shown that plastic film residue is among the main primary sources of microplastics in farmland soils. Therefore, we manufactured and selected polyethylene microplastics (PE-MPs) from plastic film and combined them with soil samples after density separation treatment, forming a spiked test soil sample, which was subjected to density separation and extraction using a ZnCl₂ solution. The experiment used visual inspection by stereo microscopy, micro-Fourier transform infrared spectroscopy, micro-Raman spectrometry, laser direct infrared imaging, and pyrolysis-gas chromatography–mass spectroscopy for the quantitative analysis of spiked soil. This study systematically assessed and compared these five common soil microplastic detection methods. The results revealed different quantitative detection accuracies across various particle size ranges. Micro-Fourier transform infrared spectroscopy showed a broader applicable particle size range (20–5000 μm) and higher detection rate (91%), while the detection range (>50 μm) and rate (70%) of visual inspection through stereo microscopy were lower. For the identification of microplastic morphology, laser direct infrared imaging performed better, whereas micro-Fourier transform infrared spectroscopy had the advantage in determining the types of polymers present over micro-Raman spectrometry. The advantages, disadvantages, and detection rates of the above five detection methods were clarified during the detection process. Considering these findings and similar studies, we suggest three combinations of the evaluated methods to optimize the detection of PE-MPs.

The absorption of microplastics (MPs; size < 5 mm) by plants has garnered increasing global attention owing to its potential implications for food safety. However, the extent to which leafy vegetables can absorb large amounts of MPs, particularly those > 1 μm, remains insufficiently demonstrated. To address this gap in knowledge, we conducted water culture experiments using water spinach (Ipomoea aquatica F.) as a model plant. The roots of water spinach were exposed to a mixed solution that contained fluorescently labeled polystyrene (PS) beads with particle sizes of 200 nm and 1 μm for 10 d. We utilized laser confocal scanning microscopy and scanning electron microscopy to record the absorption, migration, and patterns of accumulation of these large particle sizes of MPs within water spinach. Our findings revealed that micron-sized PS beads were absorbed by the roots in the presence of submicron PS beads and subsequently transported through the exosomes to accumulate to significant levels in the leaves. Short-term hydroponic experiments further indicated that high concentrations of PS bead solutions significantly inhibited the growth of water spinach owing to their large specific surface area that hindered the uptake of water and nutrients by the roots. In conclusion, both sizes of PS beads were found to be absorbed by water spinach, thereby increasing the risk associated with direct human consumption of microplastics in fruits and vegetables. This study provides valuable scientific insights to assess the pollution risks related to fruits and vegetables, as well as ensuring vegetable safety.

Multispectral imaging technology uses sensors capable of detecting spectral information across various wavelength ranges to acquire multi-channel target data. This enables researchers to collect comprehensive biological information about the observed objects or areas, including their physical and chemical characteristics. Spectral technology is widely applied in agriculture for collecting crop information and predicting yield. Over the past decade, multispectral image acquisition and information extraction from plants have provided rich data resources for scientific research, facilitating a deeper understanding of plant growth mechanisms and ecosystem function. This article presents a bibliometric analysis of the relationship between multispectral imaging and crop yield prediction, reviewing past studies and forecasting future research trends. Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. Cluster and co-citation analyses revealed the developmental trajectory of multispectral yield estimation. Our bibliometric approach offers a novel perspective to understand the current status of multispectral technology in agricultural applications. This methodology helps new researchers quickly familiarize themselves with the field’s knowledge and gain a more precise understanding of development trends and research hotspots in the domain of multispectral technology for agricultural yield estimation.

The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds.

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines.