Search Results (27)

Sulfur fumigation (SF), acid dipping (HCl treatment, HAT), and their combination (SF+HAT) are common methods for long-term preservation and color protection of litchi. However, their effects on the metabolic profile of the litchi pericarp have not been investigated. SF resulted in a yellowish-green pericarp by up-regulating lightness (L*), b*, C*, and h° but down-regulating total anthocyanin content (TAC) and a*, while HAT resulted in a reddish coloration by up-regulating a*, b*, and C* but down-regulating L*, h°, and TAC. SF+HAT recovered reddish color with similar L*, C* to SF but a*, b*, h°, and TAC between SF and HAT. Differential accumulated metabolites (DAMs) detected in HAT (vs. control) were more than those in SF (vs. control), but similar to those in SF+HAT (vs. control). SF specifically down-regulated the content of cyanidin-3-O-rutinoside, sinapinaldehyde, salicylic acid, and tyrosol, but up-regulated 6 flavonoids (luteolin, kaempferol-3-O-(6″-malonyl)galactoside, hesperetin-7-O-glucoside, etc.). Five pathways (biosynthesis of phenylpropanoids, flavonoid biosynthesis, biosynthesis of secondary metabolites, glutathione metabolism, and cysteine and methionine metabolism) were commonly enriched among the three treatments, which significantly up-regulated sulfur-containing metabolites (mainly glutathione, methionine, and homocystine) and down-regulated substrates for browning (mainly procyanidin B2, C1, and coniferyl alcohol). These results provide metabolic evidence for the effect of three treatments on coloration and storability of litchi. Full article

Honey is a sweet substance laboriously collected and crafted from nectar by bees, and since ancient times, it has been deeply cherished by humans for its unique flavor and nutritional value. Litchi honey stands out among various types of honey with its unique flavor and sweet taste, and it is particularly favored by consumers. In accordance with the testing methodologies specified in relevant Chinese national standards, we conducted an exhaustive analysis of the physicochemical properties of six litchi honey samples in Southern China. The results showed that the moisture content fell within a range of 17.18% to 22.7%, while the electrical conductivity remained below 0.28 mS/cm, and amylase activity surpassed 7.7 mL/(g·h). The fructose content varied from 36.5% to 39.6%, with glucose content ranging between 30.57% and 37.63%. The combined total of these two monosaccharides was found to be within the spectrum of 69.63% to 77.23%, and sucrose content was recorded between 0.59% and 1.15%. The F/G was between 1.05 and 1.28, the proportion of fructose in reducing sugars ranged from 51.28% to 56.22%, and the maltose content was between 1.09% and 1.51%. The HMF content was measured between 1.04 and 3.49 mg/kg. Moreover, the presence of C-4 plant sugars was absent in all tested honey samples. These results definitively demonstrate that the physicochemical attributes of all litchi honey samples align with the standards set forth by Chinese national regulations and international authorities such as CODEX. During our in-depth examination of volatile constituents, we identified 26 common compounds, with trans-linalool oxide, linalool, lilac aldehyde B, lilac aldehyde D, α-terpineol, and cedrol emerging as pivotal in crafting the unique flavor and aroma profile of litchi honey. Additionally, the detection of methyl cyclosiloxane in litchi honey has garnered our attention, necessitating a comprehensive investigation into the honey production process. In conclusion, this study not only establishes a robust scientific basis for the quality assurance and product development of litchi honey but also provides valuable reference information for consumers in their selection of honey products. Full article

Litchi is one of the ancient fruits that originated in China, renowned for its high nutrition and rich flavor, and Xianjinfeng (XJF) stands as one of the most notable varieties in terms of its flavor. Investigating the metabolic changes in taste compounds during fruit development offers deeper insights into the formation patterns of fruit quality. In this study, we conducted extensive metabonomic research on the accumulation patterns of taste compounds (carbohydrates, organic acids, and amino acids) across three developmental stages of XJF litchi. A total of 238 taste metabolites were detected. Cluster analysis and PCA revealed significant changes in metabolite composition and content across different stages, closely correlating with the developmental phase. The abundance of total taste metabolites in stage S1 was notably lower than stages S2 and S3. The total abundance of sugar continued to rise, yet monosaccharides and disaccharides exhibited distinct behaviors, highlighting the characteristic accumulation of reducing sugars. Most organic acids demonstrated a notable downward trend, whereas the abundance of most essential and flavor-contributing amino acids showed an upward trend. The number of DAMs across the three stages followed the trend of S1 vs. S3 > S1 vs. S2 > S2 vs. S3. KEGG functional annotation and enrichment revealed that amino acid biosynthesis, D-amino acid metabolism, 2-oxocarboxylic acid metabolism, glyoxylate and dicarboxylate metabolism, the pentose phosphate pathway, the tricarboxylic acid cycle, and carbon metabolism were the most significantly enriched primary metabolic pathways. More differential metabolites and metabolic pathways indicated that the critical stage from the green fruit stage to the color transition stage laid a solid foundation for litchi flavor. This experiment will offer valuable references for cultivation, breeding, processing, and consumption. Full article

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R² of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R² of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R² of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection. Full article

The accurate detection of litchi fruit cluster is the key technology of litchi picking robot. In the natural environment during the day, due to the unstable light intensity, uncertain light angle, background clutter and other factors, the identification and positioning accuracy of litchi fruit cluster is greatly affected. Therefore, we proposed a method to detect litchi fruit cluster in the night environment. The use of artificial light source and fixed angle can effectively improve the identification and positioning accuracy of litchi fruit cluster. In view of the weak light intensity and reduced image features in the nighttime environment, we proposed the YOLOv8n-CSE model. The model improves the recognition of litchi clusters in night environment. Specifically, we use YOLOv8n as the initial model, and introduce the CPA-Enhancer module with chain thinking prompt mechanism in the neck part of the model, so that the network can alleviate problems such as image feature degradation in the night environment. In addition, the VoVGSCSP design pattern in Slimneck was adopted for the neck part, which made the model more lightweight. The multi-scale linear attention mechanism and the EfficientViT module, which can be deeply divided, further improved the detection accuracy and detection rate of YOLOv8n-CSE. The experimental results show that the proposed YOLOv8n-CSE model can not only recognize litchi clusters in the night scene, but also has a significant improvement over previous models. In mAP@0.5 and F1, YOLOv8n-CSE achieved 98.86% and 95.54% respectively. Compared with the original YOLOv8n, RT-DETR-l and YOLOv10n, mAP@0.5 is increased by 4.03%, 3.46% and 3.96%, respectively. When the number of parameters is only 4.93 m, F1 scores are increased by 5.47%, 2.96% and 6.24%, respectively. YOLOv8n-CSE achieves an inference time of 36.5ms for the desired detection results. To sum up, the model can satisfy the criteria of the litchi cluster detection system for extremely accurate nighttime environment identification. Full article

Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences in scale and are occluded by leaves, reducing the accuracy of litchi detection models. Adopting traditional horizontal bounding boxes will introduce a large amount of background and overlap with adjacent frames, resulting in a reduced litchi detection accuracy. Therefore, this study innovatively introduces the use of the rotation detection box model to explore its capabilities in scenarios with occlusion and small targets. First, a dataset on litchi rotation detection in natural scenes is constructed. Secondly, three improvement modules based on YOLOv8n are proposed: a transformer module is introduced after the C2f module of the eighth layer of the backbone network, an ECA attention module is added to the neck network to improve the feature extraction of the backbone network, and a 160 × 160 scale detection head is introduced to enhance small target detection. The test results show that, compared to the traditional YOLOv8n model, the proposed model improves the precision rate, the recall rate, and the mAP by 11.7%, 5.4%, and 7.3%, respectively. In addition, four state-of-the-art mainstream detection backbone networks, namely, MobileNetv3-small, MobileNetv3-large, ShuffleNetv2, and GhostNet, are studied for comparison with the performance of the proposed model. The model proposed in this article exhibits a better performance on the litchi dataset, with the precision, recall, and mAP reaching 84.6%, 68.6%, and 79.4%, respectively. This research can provide a reference for litchi yield estimations in complex orchard environments. Full article

Litchi (Litchi chinensis Sonn.) is a tropical fruit originating from southern China that is currently cultivated in subtropical and tropical regions worldwide. Litchi anthracnose, caused by Colletotrichum fructicola, a dominant species of Colletotrichum spp., is an important disease of litchi that damages the fruits in fields and in post-harvest storage. Real-time quantitative PCR (RT-qPCR) is a common technique with which to detect the expression of and function of target genes quickly and precisely, and stable reference genes are crucial. However, there is no comprehensive information on suitable reference genes of C. fructicola present. Here, we designed eight candidate genes (GAPDH, α-tubulin, 18S, β-tubulin, EF1a, TATA, RPS5, and EF3) using RefFinder software (programs: geNorm, ΔCt, BestKeeper, and NormFinder) to investigate their reliability in the detection of C. fructicola under five different treatments (fungal development stage, temperature, UV, culture medium, and fungicide). The results showed the optimal reference genes under different conditions: EF1a and α-tubulin for developmental stage; α-tubulin and β-tubulin for temperature; α-tubulin and RPS5 for UV treatment; RPS5 and α-tubulin for culture medium; α-tubulin, GAPDH, and TATA for fungicide treatments. The corresponding expression patterns of HSP70 (Heat shock protein 70) were significantly different when the most and the least stable reference genes were selected when treated under different conditions. Our study provides the first detailed list of optimal reference genes for the analysis of gene expression in C. fructicola via RT-qPCR, which should be useful for future functional studies of target genes in C. fructicola. Full article

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning. Full article

The utilization of unmanned aerial vehicles (UAVs) for the precise and convenient detection of litchi fruits, in order to estimate yields and perform statistical analysis, holds significant value in the complex and variable litchi orchard environment. Currently, litchi yield estimation relies predominantly on manual rough counts, which often result in discrepancies between the estimated values and the actual production figures. This study proposes a large-scene and high-density litchi fruit recognition method based on the improved You Only Look Once version 5 (YOLOv5) model. The main objective is to enhance the accuracy and efficiency of yield estimation in natural orchards. First, the PANet in the original YOLOv5 model is replaced with the improved Bi-directional Feature Pyramid Network (BiFPN) to enhance the model’s cross-scale feature fusion. Second, the P2 feature layer is fused into the BiFPN to enhance the learning capability of the model for high-resolution features. After that, the Normalized Gaussian Wasserstein Distance (NWD) metric is introduced into the regression loss function to enhance the learning ability of the model for litchi tiny targets. Finally, the Slicing Aided Hyper Inference (SAHI) is used to enhance the detection of tiny targets without increasing the model’s parameters or computational memory. The experimental results show that the overall $AP$ value of the improved YOLOv5 model has been effectively increased by 22%, compared to the original YOLOv5 model’s $AP$ value of 50.6%. Specifically, the $A{P}_s$ value for detecting small targets has increased from 27.8% to 57.3%. The model size is only 3.6% larger than the original YOLOv5 model. Through ablation and comparative experiments, our method has successfully improved accuracy without compromising the model size and inference speed. Therefore, the proposed method in this paper holds practical applicability for detecting litchi fruits in orchards. It can serve as a valuable tool for providing guidance and suggestions for litchi yield estimation and subsequent harvesting processes. In future research, optimization can be continued for the small target detection problem, while it can be extended to study the small target tracking problem in dense scenarios, which is of great significance for litchi yield estimation. Full article

To investigate the feasibility of developing litchi leaves as silage, we determined the fermentation quality of four varieties of litchi leaves (including “Wanpu”, “Wuyejiu”, “Tongzai” and “Zhuangyuanhong”) ensiled with or without Lactobacillus plantarum on day 3, 7, 14 and 30. The in vitro dry matter digestibility and gas production of litchi leaves silages were also determined after 30 days of ensiling. The results showed that Lactobacillus plantarum significantly reduced pH value (p < 0.01), inhibited coliform bacteria, and reduced the production of ammonia nitrogen (p < 0.01) in all the four kinds of litchi leaves silage. Moreover, Lactobacillus plantarum treated litchi leaves (“Wanpu” and “Zhuangyuanhong”) had lower yeasts than the untreated litchi leaves during ensiling. The number of molds in Lactobacillus plantarum treated groups (“Tongzai” and “Zhuangyuanhong”) was below the detected level after 30 days ensiling, which was lower than that of the untreated groups. The addition of Lactobacillus plantarum also contributed to improving IVDMD and markedly reduced (p < 0.01) gas production of all litchi leaves silages. Conclusions: Lactobacillus plantarum can improve the fermentation quality and in vitro digestion characteristics of litchi leaves silage. Developing litchi leaves as silage material is a feasible way to recycle litchi leaves. Full article

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control. Full article

Litchi florescence has large flower spikes and volume; reasonable control of the ratio of male to female litchi flowers is the key operational aspect of litchi orchards for preserving quality and increasing production. To achieve the rapid detection of male and female litchi flowers, reduce manual statistical errors, and meet the demand for accurate fertilizer regulation, an intelligent detection method for male and female litchi flowers suitable for deployment to low-power embedded platforms is proposed. The method uses multi-teacher pre-activation feature distillation (MPFD) and chooses the relatively complex YOLOv4 and YOLOv5-l as the teacher models and the relatively simple YOLOv4-Tiny as the student model. By dynamically learning the intermediate feature knowledge of the different teacher models, the student model can improve its detection performance by meeting the embedded platform application requirements such as low power consumption and real-time performance. The main objectives of this study are as follows: optimize the distillation position before the activation function (pre-activation) to reduce the feature distillation loss; use the LogCosh-Squared function as the distillation distance loss function to improve distillation performance; adopt the margin-activation method to improve the features of the teacher model passed to the student model; and propose to adopt the Convolution and Group Normalization (Conv-GN) structure for the feature transformation of the student model to prevent effective information loss. Moreover, the distilled student model is quantified and ported for deployment to a field-programmable gate array (FPGA)-embedded platform to design and implement a fast, intelligent detection system for male and female litchi flowers. The experimental results show that compared with an undistilled student model, the mAP of the student model obtained after MPFD feature distillation is improved by 4.42 to 94.21%; the size of the detection model ported and deployed to the FPGA-embedded platform is 5.91 MB, and the power consumption is only 10 W, which is 73.85% and 94.54% lower than that of the detection models on the server and PC platforms, respectively, and it can better meet the application requirements of rapid detection and accurate statistics of male and female litchi flowers. Full article

The fruit of Litchi chinensis contains high levels of proanthocyanidins (PAs) in the pericarp. These substances can serve as substrates of laccase-mediated rapid pericarp browning after the fruit is harvested. In this study, we found that the major PAs in litchi pericarp were (−)-epicatechin (EC) and several procyanidins (PCs), primarily PC A2, B2, and B1, and the EC and the PC content decreased with the development of the fruit. RNA-seq analysis showed that 43 early and late structure genes related to flavonoid/PA biosynthesis were expressed in the pericarp, including five ANTHOCYANIDIN REDUCTASE (ANR), two LEUCOANTHOCYANIDIN REDUCTASE (LAR), and two ANTHOCYANIDIN SYNTHASE (ANS) genes functioning in the PA biosynthesis branch of the flavonoid pathway. Among these nine PA biosynthesis-related genes, ANR1a, LAR1/2, and ANS1 were highly positively correlated with changes in the EC/PC content, suggesting that they are the key PA biosynthesis-related genes. Several transcription factor (TF) genes, including MYB, bHLH, WRKY, and AP2 family members, were found to be highly correlated with ANR1a, LAR1/2, and ANS1, and their relevant binding elements were detected in the promoters of these target genes, strongly suggesting that these TF genes may play regulatory roles in PA biosynthesis. In summary, this study identified the candidate key structure and regulatory genes in PA biosynthesis in litchi pericarp, which will assist in understanding the accumulation of high levels of browning-related PA substances in the pericarp. Full article

Hydrogen (H₂) has gradually become a substitute for traditional energy, but its potential danger cannot be ignored. In this study, litchi-like g-C₃N₄/In₂O₃ composites were synthesized by a hydrothermal method and used to develop H₂ sensors. The morphology characteristics and chemical composition of the samples were characterized to analyze the gas-sensing properties. Meanwhile, a series of sensors were tested to evaluate the gas-sensing performance. Among these sensors, the sensor based on the 3 wt% g-C₃N₄/In₂O₃ (the mass ratio of g-C₃N₄ to In₂O₃ is 3:100) showeds good response properties to H₂, exhibiting fast response/recovery time and excellent selectivity to H₂. The improvement in the gas-sensing performance may be related to the special morphology, the oxygen state and the g-C₃N₄/In₂O₃ heterojunction. To sum up, a sensor based on 3 wt% g-C₃N₄/In₂O₃ exhibits preeminent performance for H₂ with high sensitivity, fast response, and excellent selectivity. Full article

Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots. Full article