Fresh-cut fruits and vegetables have become more popular among consumers because of their nutritional value and convenience. However, the lower shelf life of fresh-cut fruits and vegetables due to processing and mechanical damage is a critical factor affecting their market expansion, and advances in preservation technology are needed to prolong their shelf life. Some traditional chemical preservatives are disliked by health-seeking consumers because of worries about toxicity. Chemical preservation is inexpensive and highly efficient, but sometimes it carries risks for human health. Biological preservation methods are safer and more appealing, but they are not applicable to large-scale production. Physical fresh-keeping methods have been used for the storage and transportation of fresh-cut fruits and vegetables due to the ease of application. This review discusses current research in fresh-keeping technology for the preservation of fresh-cut fruits and vegetables. Preservation methods include low temperature, modified atmosphere packaging, cold plasma, pulsed light, ultrasonics, ultraviolet light, and ozonated water. As promising alternatives to chemical methods, these novel processes have been evaluated singly or combined with natural preservatives or other methods to extend the shelf life of fresh-cut fruits and vegetables and to provide references and assessments for further development and application of fresh-cut fruit and vegetable preservation technology.

With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology.

The identification and enumeration of peach seedling fruits are pivotal in the realm of precision agriculture, greatly influencing both yield estimation and agronomic practices. This study introduces an innovative, lightweight YOLOv8 model for the automatic detection and quantification of peach seedling fruits, designated as YOLO-Peach, to bolster the scientific rigor and operational efficiency of orchard management. Traditional identification methods, which are labor-intensive and error-prone, have been superseded by this advancement. A comprehensive dataset was meticulously curated, capturing the rich characteristics and diversity of peach seedling fruits through high-resolution imagery at various times and locations, followed by meticulous preprocessing to ensure data quality. The YOLOv8s model underwent a series of lightweight optimizations, including the integration of MobileNetV3 as its backbone, the p2BiFPN architecture, spatial and channel reconstruction convolution, and coordinate attention mechanism, all of which have significantly bolstered the model’s capability to detect small targets with precision. The YOLO-Peach model excels in detection accuracy, evidenced by a precision and recall of 0.979, along with an mAP50 of 0.993 and an mAP50-95 of 0.867, indicating its superior capability for peach sapling identification with efficient computational performance. The findings underscore the model’s efficacy and practicality in the context of peach seedling fruit recognition. Ablation studies have shed light on the indispensable role of each component, with MobileNetV3 streamlining the model’s complexity and computational load, while the p2BiFPN architecture, ScConv convolutions, and coordinate attention mechanism have collectively enhanced the model’s feature extraction and detection precision for minute targets. The implications of this research are profound, offering a novel approach to peach seedling fruit recognition and serving as a blueprint for the identification of young fruits in other fruit species. This work holds significant theoretical and practical value, propelling forward the broader field of agricultural automation.

China has the largest apple-growing area and fresh fruit production in the world; however, water shortages and low fertilizer utilization rates have restricted agricultural development. It is a major challenge to obtain scientific and reasonable irrigation and fertilization systems for young apple trees in semi-arid regions of northern China. A 2-year field bucket experiment with four irrigation levels of W1 (75–90% Fs, where Fs is the field water holding capacity), W2 (65–80% Fs), W3 (55–70% Fs), and W4 (45–60% Fs), and three fertilizer levels of F1 (27-9-9 g N-P₂O₅-K₂O), F2 (18-9-9 g N-P₂O₅-K₂O), and F3 (9-9-9 g N-P₂O₅-K₂O) was conducted in 2019 and 2020, so as to explore the effects of different water and fertilizer treatments on the growth and physiological characteristics of young apple trees. The results showed that the plant growth, leaf area, and dry matter of young apple trees at each growing period reached maximum values under F1W2, and they showed a positive linear relationship with relative chlorophyll content (SPAD), net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), water consumption, and water use efficiency (WUE). With the growth of young apple trees, water-fertilizer coupling could significantly increase the leaf SPAD of young apple trees. Pn, Tr, and Gs reached the maximum value under F1W1, and although they decreased under F1W2, the water use efficiency increased by 2.3–25.7% and 4.0–23.8% under F1W2 compared with other treatments in two years, respectively. The water consumption of young apple trees increased with the increase of irrigation and fertilizer, and both dry matter and water productivity reached the maximum value under F1W2, which increased by 0.8%, 14.6% in 2019, and 0.6%, 11.1% in 2020 compared with F1W1, while water consumption decreased by 12.2% and 9.4% in both years. In conclusion, F1W2 treatment (soil moisture was controlled at 65–80% of field water holding capacity, and N-P₂O₅-K₂O was controlled at 27-9-9 g) was the best coupling mode of water and fertilizer for young apple trees in semi-arid areas of northern China.

Reference crop evapotranspiration (ET₀) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET₀ estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET₀ value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R² range 0.74–0.98) are more accurate than 4 empirical models (R² range 0.55–0.84), and ET_0-PSO-HG has the best accuracy, followed by ET_0-ABC-HG and ET_0-DE-HG, with average R² of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET_0-PSO-HG also has the highest accuracy, followed by ET_0-ABC-HG and ET_0-DE-HG, with median R² of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET₀ estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET₀ in humid regions in southwest China regions.

Plant protection UAVs are becoming the preferred plant protection method for agricultural pest control. At present, the evaluation of droplet distribution in aerial spraying is collected and evaluated after the completion of prevention and control operations, and there is a lack of real-time evaluation methods. Based on the flight parameter during the UAV plant protection process, real-time estimation of droplet distribution is the key to solving this problem and further improving the effectiveness of aerial spraying. This study proposes a merging algorithm for arbitrary polygonal regions, meshing the boundaries of the region, divide the mesh segments based on the overlapping meshes between the two regions, and connect the valid mesh connection segments of the two regions according to certain rules to obtain the intersection, union, and residual operation results between the regions. Afterwards, software based on this algorithm was developed and applied to generate spraying coverage regions, leakage spray regions, and repeated spray regions. The experimental results on theoretical and irregular routes show that the algorithm can accurately generate droplet distribution regions. The error of the calculation results with a mesh scale of 0.05 m is within 7‰, and the operating speed is above 30 Hz, meeting the real-time requirements. The smaller the mesh scale is, the higher the accuracy of the calculation results is, but the slower the calculation speed. Therefore, in practical applications, it is necessary to choose an appropriate mesh scale based on hardware computing power and accuracy level requirements. This study solves the problem of cumulative calculation of droplet distribution during the operation of plant protection UAVs, providing a basis for objectively evaluating the operation quality of plant protection UAVs and optimizing the setting of operation parameters.