Search Results (192)

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

The molecular identification of valuable genotypes is an important problem of germplasm management. In this study, we evaluated the potential of 11 universal primer pairs for the DNA barcoding of locally derived cultivars of subtropical crops (actinidia, feijoa, citrus, and tea). A total of 47 accessions (elite cultivars, forms, and breeding lines) of these four genera were included in the study. The efficiency of the following universal primers was assessed using Sanger sequencing: ITS-p5/ITS-u4, ITS-p5/ITS-u2, ITS-p3/ITS-u4, 23S,4.5S&5S, 16S, petB/petD, rpl23/rpl2.l, rpl2 intron, rpoC1 intron, trnK intron, and trnE-UUC/trnT-GUU. Among these primers, trnE-UUC/trnT-GUU showed greater intraspecific polymorphisms, while rpl2 intron and 16S displayed the lowest polymorphism levels in all crops. In addition, the 23S,4.5S & 5S, and rpoC1 intron were efficient for intraspecific analysis of tea and actinidia species. Using five efficient chloroplast primers, a total of 22/6 SNPs/InDels were observed in tea accessions, 45/17 SNPs/InDels in actinidia, 23/3 SNPs/InDels in mandarins, and 5/4 SNPs/InDels in feijoa. These results will be useful for the further development of DNA barcodes of related accessions. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

Citrus diseases can lead to surface defects on citrus fruits, adversely affecting their quality. This study aims to accurately identify citrus defects against varying backgrounds by focusing on four types of diseases: citrus black spot, citrus canker, citrus greening, and citrus melanose. We propose an improved YOLOv10-based disease detection method that replaces the traditional convolutional layers in the Backbone network with LDConv to enhance feature extraction capabilities. Additionally, we introduce the GFPN module to strengthen multi-scale information interaction through cross-scale feature fusion, thereby improving detection accuracy for small-target diseases. The incorporation of the DAT mechanism is designed to achieve higher efficiency and accuracy in handling complex visual tasks. Furthermore, we integrate the AFPN module to enhance the model’s detection capability for targets of varying scales. Lastly, we employ the Slide Loss function to adaptively adjust sample weights, focusing on hard-to-detect samples such as blurred features and subtle lesions in citrus disease images, effectively alleviating issues related to sample imbalance. The experimental results indicate that the enhanced model YOLOv10-LGDA achieves impressive performance metrics in citrus disease detection, with accuracy, recall, mAP@50, and mAP@50:95 rates of 98.7%, 95.9%, 97.7%, and 94%, respectively. These results represent improvements of 4.2%, 3.8%, 4.5%, and 2.4% compared to the original YOLOv10 model. Furthermore, when compared to various other object detection algorithms, YOLOv10-LGDA demonstrates superior recognition accuracy, facilitating precise identification of citrus diseases. This advancement provides substantial technical support for enhancing the quality of citrus fruit and ensuring the sustainable development of the industry. Full article

Given the serious economic burden that citrus diseases impose on fruit farmers and related industries, achieving rapid and accurate disease detection is particularly crucial. In response to the challenges posed by resource-limited platforms and complex backgrounds, this paper designs and proposes a lightweight method for the identification and localization of citrus diseases based on the RT-DETR-r18 model—GYS-RT-DETR. This paper proposes an optimization method for target detection that significantly enhances model performance through multi-dimensional technology integration. First, this paper introduces the following innovations in model structure: (1) A Gather-and-Distribute Mechanism is introduced in the Neck section, which effectively enhances the model’s ability to detect medium to large targets through global feature fusion and high-level information injection.(2) Scale Sequence Feature Fusion (SSFF) is used to optimize the Neck structure to improve the detection performance of the model for small targets in complex environments. (3) The Focaler-ShapeIoU loss function is used to solve the problems of unbalanced training samples and inaccurate positioning. Secondly, the model adopts two model optimization strategies: (1) The Group_taylor local pruning algorithm is used to reduce memory occupation and the number of computing parameters of the model. (2) The feature-logic knowledge distillation framework is proposed and adopted to solve the problem of information loss caused by the structural difference between teachers and students, and to ensure a good detection performance, while realizing the lightweight character of the model. The experimental results show that the GYS-RT-DETR model has a precision of 79.1%, a recall of 77.9%, an F1 score of 78.0%, a model size of 23.0 MB, and an mAP value of 77.8%. Compared to the original model, the precision, recall, the F1 score, the mAP value, and the FPS value have improved by 3.5%, 5.3%, 5.0%, 5.3%, and 10.3 f/s, respectively. Additionally, the memory usage of the GYS-RT-DETR model has decreased by 25.5 MB compared to the original model. The GYS-RT-DETR model proposed in this article can effectively detect various citrus diseases in complex backgrounds, addressing the time-consuming nature of manual detection and improving the accuracy of model detection, thereby providing an effective theoretical basis for the automated detection of citrus diseases. Full article

Lemon, widely used in food, medicine, cosmetics, and other industries, has considerable value as a commodity and horticultural product. Previous research has shown that the fungus Diaporthe citri infects several citrus species, including mandarin, lemon, sweet orange, pomelo, and grapefruit, in China. Although D. citri has been reported to cause melanose disease in lemons in China, key pathological evidence, such as Koch’s postulates fulfillment on lemon fruits and detailed morphological characterization, is still lacking. In May 2018, fruits, leaves, and twigs were observed to be infected with melanose disease in lemon orchards in Chongqing municipality in China. The symptoms appeared as small black discrete spots on the surface of fruits, leaves, and twigs without obvious prominent and convex pustules. D. citri was isolated consistently from symptomatic organs and identified provisionally based on the morphological characteristics. The identification was confirmed using sequencing and multigene phylogenetic analysis of ITS, TUB, TEF, HIS, and CAL regions. Pathogenicity tests were performed using a conidium suspension, and melanose symptoms similar to those observed in the field were reproduced. To our knowledge, this study provides the first comprehensive evidence for D. citri as a causal agent of melanose disease in lemons in China, including morphological characterization and pathogenicity assays on lemon fruits. This report broadens the spectrum of hosts of D. citri in China and provides useful information for the management of melanose in lemons. Full article

Geographical origins and varietal characteristics can significantly affect the quality of Citri Reticulatae Pericarpium (Chenpi), making rapid and accurate identification essential for consumer protection. To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. Hyperspectral data were collected from 15 Chenpi varieties (citrus peel) across 13 major production regions in China using three dataset configurations: exocarp-facing-upward (Z), endocarp-facing-upward (F), and a fused dataset combining random orientations (ZF). Convolutional neural networks (CNNs) were developed and compared with conventional machine learning models, including partial least-squares discriminant analysis (PLS-DA), support vector machines (SVMs), and a multilayer perceptron (MLP). The CNN model achieved 96.39% accuracy for varietal classification with the ZF dataset, while the Z-PLSDA model optimized with second derivative (D2) preprocessing and competitive adaptive reweighted sampling (CARS) feature selection attained 91.67% accuracy in geographical origin discrimination. Feature wavelength selection strategies, such as CARS, simplified the model complexity while maintaining a classification performance comparable to that of the full-spectrum models. These findings demonstrated that HSI combined with deep learning could provide a rapid, nondestructive, and cost-effective solution for Chenpi quality assessment and origin traceability. Full article

Citric acid serves as the principal organic acid in citrus fruits, with its concentration critically determining fruit flavor and market acceptability. Isocitrate dehydrogenase (IDH), a key enzyme in citric acid metabolism, mediates the conversion of citrate to α-ketoglutarate. This study cloned six candidate genes encoding IDH from grapefruit (Citrus paradisi). Bioinformatics analysis showed that all six genes contained the typical characteristic structure of IDH. Gene expression analysis found that CpNADP-IDH1 is highly expressed in mature and low-acid varieties. Overexpression of CpNADP-IDH1 significantly increased IDH enzyme activity and decreased citric acid content in transgenic grapefruit callus. These results showed that at least six genes encoding IDH exist in grapefruit, among which CpNADP-IDH1 catalyzes the decomposition of citric acid and regulates the organic acid content in fruits at maturity. CpNADP-IDH1 can be used as a candidate gene for molecular breeding of low-acid citrus varieties and as an essential target gene for developing citrus cultivation technology for reducing acid content. Full article

Citrus pests and diseases severely impact fruit yield and quality. However, existing object detection models face limitations in complex backgrounds, target occlusion, and small target recognition, and they struggle to be efficiently deployed on resource-constrained devices. To address these issues, this study proposes a lightweight pest and disease detection model, YOLOv11-RDTNet, based on the improved YOLOv11n. This model integrates multi-scale features and attention mechanisms to enhance recognition performance in complex scenarios, while adopting a lightweight design to reduce computational costs and improve deployment adaptability. The model introduces three key enhancement features: First, shallow RFD (SRFD) and deep RFD (DRFD) downsampling modules replace traditional convolution modules, improving image feature extraction accuracy and robustness. Second, the Dynamic Group Shuffle Transformer (DGST) module replaces the original C3k2 module, reducing the model’s parameter count and computational demand, further enhancing efficiency and performance. Lastly, the lightweight Task Align Dynamic Detection Head (TADDH) replaces the original detection head, significantly reducing the parameter count and improving accuracy in small-object detection. After processing the collected images, we obtained 1382 images and constructed a dataset containing five types of citrus pests and diseases: anthracnose, canker, yellow vein disease, coal pollution disease, and leaf miner moth. We applied data augmentation on the dataset and conducted experimental validation. Experimental results showed that the YOLOv11-RDTNet model had a parameter count of 1.54 MB, an mAP50 of 87.0%, and a model size of 3.4 MB. Compared to the original YOLOv11 model, the YOLOv11-RDTNet model reduced the parameter count by 40.3%, improved mAP50 by 4.8%, and reduced the model size from 5.5 MB to 3.4 MB. This model not only improved detection accuracy and reduced computational load but also achieved a balance in performance, size, and speed, making it more suitable for deployment on mobile devices. Additionally, the research findings provided an effective tool for citrus pest and disease detection with small sample sizes, offering valuable insights for citrus pest and disease detection in agricultural practices. Full article

Timely detection and prevention of citrus leaf diseases and pests are crucial for improving citrus yield. To address the issue of low efficiency in citrus disease and pest detection, this paper proposes a lightweight detection model named PEW-YOLO. First, the PP-LCNet backbone is optimized using a novel GSConv convolution, and a lightweight PGNet backbone is introduced to reduce model parameters while enhancing detection performance. Next, the C2f_EMA module, which integrates efficient multi-scale attention (EMA), replaces the original C2f module in the neck, thereby improving feature fusion capabilities. Finally, the Wise-IoU loss function is employed to address the challenge of identifying low-quality samples, further improving both convergence speed and detection accuracy. Experimental results demonstrate that PEW-YOLO achieves a 1.8% increase in mAP50, a 32.2% reduction in parameters, and a detection speed of 1.6 milliseconds per frame on the citrus disease and pest dataset, thereby meeting practical real-time detection requirements. Full article

In agriculture, machine learning (ML) and deep learning (DL) have increased significantly in the last few years. The use of ML and DL for image classification in plant disease has generated significant interest due to their cost, automatization, scalability, and early detection. However, high-quality image datasets are required to train robust classifier models for plant disease detection. In this work, we have created an image dataset of 649 orange leaves divided into two groups: control (n = 379) and huanglongbing (HLB) disease (n = 270). The images were acquired with several smartphone cameras of high resolution and processed to remove the background. The dataset enriches the information on characteristics and symptoms of citrus leaves with HLB and healthy leaves. This enhancement makes the dataset potentially valuable for disease identification through leaf segmentation and abnormality detection, particularly when applying ML and DL models. Full article

The citrus industry plays a pivotal role in modern agriculture. With the expansion of citrus plantations, the intelligent detection and prevention of diseases and pests have become essential for advancing smart agriculture. Traditional citrus leaf disease identification methods primarily rely on manual observation, which is often time-consuming, labor-intensive, and prone to inaccuracies due to inherent asymmetries in disease manifestations. This work introduces CBACA-YOLOv5, an enhanced YOLOv5s-based detection algorithm designed to effectively capture the symmetric and asymmetric features of common citrus leaf diseases. Specifically, the model integrates the convolutional block attention module (CBAM), which symmetrically enhances feature extraction across spatial and channel dimensions, significantly improving the detection of small and occluded targets. Additionally, we incorporate coordinate attention (CA) mechanisms into the YOLOv5s C3 module, explicitly addressing asymmetrical spatial distributions of disease features. The CARAFE upsampling module further optimizes feature fusion symmetry, enhancing the extraction efficiency and accelerating the network convergence. Experimental findings demonstrate that CBACA-YOLOv5 achieves an accuracy of 96.1% and a mean average precision (mAP) of 92.1%, and improvements of 0.6% and 2.3%, respectively, over the baseline model. The proposed CBACA-YOLOv5 model exhibits considerable robustness and reliability in detecting citrus leaf diseases under diverse and asymmetrical field conditions, thus holding substantial promise for practical integration into intelligent agricultural systems. Full article

Serotonin N-acetyltransferase (SNAT) is a crucial enzyme in the melatonin synthesis pathway, playing an essential role in both melatonin biosynthesis and plant resistance to abiotic stress. A bioinformatics approach was employed to identify the members of the citrus SNAT gene family and to analyze their physicochemical properties, gene structure, conserved domains, phylogenetic relationships, and promoter cis-acting elements. Additionally, the tissue-specific expression of trifoliate orange SNAT family members and their expression patterns under stress conditions were examined. This study identified 21 members of the SNAT gene family across five citrus genomes, distributed over five chromosomes, with the majority predicted to localize within chloroplasts. These genes were characterized by having between 1 and 8 exons, 0 and 7 introns, 1 and 2 conserved domains, and 5 and 8 motifs. Phylogenetic analysis classified the genes into four subgroups, demonstrating significant collinearity with SNAT genes in rice. Analysis of the promoter regions revealed 32 cis-acting elements, with those responsive to light, abscisic acid, and drought being the most common. Expression analysis of SNAT genes in trifoliate orange indicated tissue specificity, with the highest expression levels detected in leaves. Quantitative real-time PCR analysis showed that the PtrSNAT1 gene was notably upregulated under various stress conditions, suggesting its role in stress response. Overall, these findings provide critical insights for further functional studies of citrus SNAT genes in relation to abiotic stress responses. Moreover, the PtrSNAT1 gene represents a potential target for developing rootstocks with enhanced resistance to abiotic stress. Full article

The citrus industry contributes to the cultivation of one of the most important fruit crops globally. However, citrus trees are susceptible to numerous Bisifusarium, Fusarium, and Neocosmospora-linked diseases, with dry root rot posing a serious threat to citrus orchards worldwide. These infections are exacerbated by biotic and abiotic stresses, leading to increased disease incidence. Healthy trees unexpectedly wilt and fall, exhibiting symptoms such as chlorosis, dieback, necrotic roots, root rot, wood discolouration, and eventual decline. Research indicates that the disease is caused by a complex of species from the Nectriaceae family, with Neocosmospora solani being the most prominent. To improve treatment and management strategies, further studies are needed to definitively identify these phytopathogens and understand the conditions and factors associated with Bisifusarium, Fusarium, and Neocosmospora-related diseases in citrus. This review focuses on the epidemiology and symptomatology of Fusarium and Neocosmospora species, recent advances in molecular techniques for accurate phytopathogen identification, and the molecular mechanisms of pathogenicity and resistance underlying Fusarium and Neocosmospora–citrus interactions. Additionally, the review highlights novel alternative methods, including biological control agents, for disease control to promote environmentally friendly and sustainable agricultural practices. Full article

Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for enhanced citrus disease detection. It introduces the DR module structure for effective feature enhancement and the Detect_Shared architecture for parameter efficiency. Evaluated on public and orchard-collected datasets, YOLOv8n-DE achieves 97.6% classification accuracy, 91.8% recall, and 97.3% mAP, with a 90.4% mAP for challenging diseases. Compared to the original YOLOv8, it reduces parameters by 48.17%, computational load by 59.26%, and model size by 41.94%, while significantly decreasing classification and regression errors, and false positives/negatives. YOLOv8n-DE offers outstanding performance and lightweight advantages for citrus disease detection, supporting precision agriculture development in orchards. Full article