Search Results (6)

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Full article

In Gannan navel orange (Citrus sinensis) orchards—a typical sloped farmland ecosystem—selected native grasses outperform conventional green manure due to their stronger ecological adaptability and lower management requirements. However, few studies have investigated how native grasses enhance soil organic carbon and nitrogen contents at the soil aggregate level. A 5-year field study was carried out to analyze the impacts of the native grasses practice on the accumulation of soil organic carbon and nitrogen and the physicochemical properties and microbial communities of soil aggregates in navel orange orchards. Three treatments were tested: (i) clean tillage (CK); (ii) intercropping Centella asiatica (L.) Urban (CA); (iii) intercropping Stellaria media (L.) Cvr. (SM). Our work found that, compared to CK, the soil physical properties improved under the long-term management of native grasses, and the content of nutrients in the soil increased. The contents of SOC (+118.3–184.2%) and total nitrogen (TN) (+73.3–81.5%) changed significantly. The proportion of soil macro-aggregates and the stability of soil aggregates increased, and the contents of SOC and TN in the soil aggregates increased. In addition, under the long-term management of native grasses, the community diversity of beneficial microbes and the abundance of functional genes related to nitrogen cycling increased significantly in the soil aggregates. Native grasses increased the content of nutrients in the soil aggregates by increasing aggregate stability and the abundance of related microorganisms, altering the microbial community structure, and increasing the abundance of related genes for nutrient cycling, thereby enhancing the sequestration of SOC and TN in topsoil. Our results will provide a theoretical basis for the carbon enhancement and fertilization of native grasses as green manure in navel orange orchards and their popularization and application. Full article

In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits. Full article

Gannan navel orange (Citrus sinensis Osbeck cv. Newhall) is an economically important fruit, but postharvest loss occurs easily during storage. In this study, the effects of different temperatures, light illuminations, and low-temperature plasma treatments on the water loss and quality of the Gannan navel orange were investigated. The fruit began to rot after 90 d of storage at 5 °C and 20–45 d at 26 °C. Navel oranges stored at 26 °C had 7.2-fold and 3.1-fold higher rates of water loss at the early and late storage stages, respectively, as compared with those stored at 5 °C. Storage at 5 °C decreased the contents of total soluble solids at the early storage stage and the contents of titratable acids at the late storage stage, whereas storage at 26 °C decreased the contents of total soluble solids at the late storage stage and the contents of titratable acids at the early storage stage, respectively. Application of low-temperature plasma produced by air ionization for 6 min, or continuous blue or red light illumination significantly inhibited water loss within 7 and 21 d of storage at 22 °C, respectively, but exhibited no significant effect on fruit quality. Furthermore, the low-temperature plasma treatment protected against fruit rot. Thus, treatment with low-temperature plasma followed by storage at a low temperature under continuous red or blue light illumination was of potential value as a green technology for preserving Gannan navel orange during storage. Full article

Gannan navel orange and Jinggang pomelo, belonging to the genus Citrus, are good sources of phenolic compounds, which are mainly concentrated in the peel. These phenolic compounds are considered promising in the prevention and treatment of non-alcoholic fatty liver disease (NAFLD). In order to maximize nutrients retention and bioactivity in the peel, pomelo peel and orange peel were processed using freeze-drying technology and mixed in the ratio (pomelo peel powder 50% and orange peel powder 50%) to make citrus peel powder (CPP). The purpose of this study was to explore new strategies and mechanisms associated with the consumption of CPP to alleviate nonalcoholic fatty liver injury, lipid metabolism disorders, and gut microbiota dysbiosis in obese mice induced by high-fat diet (HFD). The results showed that after 12 weeks of CPP administration, CPP supplementation had a strong inhibitory effect on HFD-induced weight gain, hepatic fat accumulation, dyslipidemia, and the release of pro-inflammatory cytokines. In particular, CPP modulates the composition of the intestinal flora, such as increasing the relative abundance of phylum Firmicutes, genus Faecalibaculum, genus Lactobacillus, genus Dubosiella, and genus Lachnospiraceae_NK4A136_ group and decreasing the relative abundance of phylum Bacteroidota, genus Helicobacter, and genus Bacteroides. These results suggest that CPP has a preventive effect on NAFLD, which can be related to the regulation of intestinal flora. Full article

China is one of the leading producers of citrus in the world. Gannan in Jiangxi Province is the top navel orange producing area in China. In the present study, an essential oil was prepared by cold pressing of Gannan navel orange peel followed by molecular distillation. Its chemical composition was analyzed by GC-MS. Twenty four constituents were identified, representing 97.9% of the total oil. The predominant constituent was limonene (74.6%). The anticancer activities of this orange essential oil, as well as some of its major constituents, were investigated by MTT assay. This essential oil showed a positive effect on the inhibition of the proliferation of a human lung cancer cell line A549 and prostate cancer cell line 22RV-1. Some of the oil constituents displayed high anticancer potential and deserve further study. Full article