Search Results (100)

Alternaria is a widespread genus and a diverse taxonomic group of fungi, whose members exhibit a wide range of ecological roles, from endophytes and saprophytes to potent plant pathogens, and in some cases, to opportunistic pathogens or allergens affecting humans. Their high adaptability to various environmental conditions determines their widespread distribution and resilience. A key feature of the genus Alternaria is its substantial species diversity. According to the Species Fungorum database, it currently comprises 792 registered species, which are grouped into 29 sections. It should be noted that this number reflects the current state of taxonomic classification and is subject to ongoing revision. The ecological role of representatives of this genus is particularly relevant in the context of agriculture, as many species are pathogens and causative agents of Alternaria leaf spot in important agricultural plants such as tomatoes, potatoes, apples, wheat, and others. This disease causes significant economic losses. At the same time, some strains demonstrate potential for use in biotechnology due to their ability to produce biologically active metabolites. This review examines the taxonomy, morphological characteristics, ecological role, pathogenicity, and control methods of fungi of the genus Alternaria, as well as their biotechnological potential. Full article

Fusarium spp. cause devastating crop diseases and produce carcinogenic mycotoxins such as fumonisins, threatening global food safety and human health. In this study, Trichoderma longibrachiatum A25011, isolated from apples in Aksu, Xinjiang, exhibited significant antagonistic activity with mycelial growth inhibition rates of 54.52% against F. verticillioides 48.62% against F. proliferatum, and 58.22% against F. oxysporum in confrontation assays. Enzyme activity detection revealed high chitinase (583.21 U/mg protein) and moderate cellulase (43.92 U/mg protein) production, which may have the capacity to degrade fungal cell walls. High-Performance Liquid Chromatography–Mass Spectrometry (HPLC-MS/MS) analyses enabled the quantification of fungal hormones including gibberellin A3 (GA3, 2.44 mg/L), cytokinins (cis-zeatin riboside (CZR): 0.69 mg/L; trans-zeatin riboside (TZR): 0.004 mg/L; kinetin: 0.006 mg/L), and auxins (indole-3-acetic acid (IAA): 0.35 mg/L; abscisic acid: 0.06 mg/L). Application of a T. longibrachiatum A25011 spore suspension around the roots of peanut plants enhanced growth by 13.20% (height), 5.65% (stem and leaf biomass), and 39.13% (root biomass). Notably, A25011 reduced F. proliferatum-derived fumonisin accumulation in rice-based cultures by 93.58% (6 d) and 99.35% (10 d), suggesting biosynthetic suppression. The results demonstrated that T. longibrachiatum strain A25011 exhibited excellent biocontrol capability against Fusarium spp., proving its dual role in simultaneously suppressing fungal growth and fumonisin accumulation while promoting plant growth. T. longibrachiatum A25011 could be applied as a multifunctional biocontrol agent in sustainable agriculture in the future. Full article

Glomerella leaf spot (GLS), mainly caused by Colletotrichum fructicola, is a destructive disease of apple. However, the underlying pathogenesis mechanisms of GLS are still largely obscure. Previous infection transcriptome analysis showed that transcription factor CfGti1 was induced during leaf infection. The present study confirms that the CfGti1 gene is strongly expressed in conidia and early infection. To identify functions performed, we generated gene deletion mutant ΔCfGti1 by homologous recombination. Phenotypic analysis revealed that ΔCfGti1 lost pathogenicity to apple leaves by blocking appressorium-mediated host penetration, although penetration pegs still developed on cellophane. In addition, ΔCfGti1 colonization and hyphal extension in wounded apple fruit were dramatically decreased. The ΔCfGti1 mutant exhibited defects in growth and development of hyphae, which may be partly responsible for its inability to colonize apple. Comparative transcriptome and qRT-PCR analyses suggested that CfGti1 regulated appressorium-mediated host penetration by modulating genes related to metabolism of appressorial lipid droplets. Interestingly, CfGti1 also regulated the expression of ybtS and AKT1 or AFT1-1 related to biosynthesis of AK and AF host-specific toxins. This study demonstrated that CfGti1 is a pivotal regulator for apple GLS pathogenesis in C. fructicola. Full article

The increasing complexity of plant disease manifestations, especially in cases of multiple simultaneous infections, poses significant challenges to sustainable agriculture. To address this issue, we introduce the Apple Leaf Mixed Disease Recognition (ALMDR) model, a novel multi-task learning approach specifically designed for identifying and quantifying mixed disease infections in apple leaves. ALMDR comprises four key modules: a Group Feature Pyramid Network (GFPN) for multi-scale feature extraction, a Multi-Label Classification Head (MLCH) for disease type prediction, a Leaf Segmentation Head (LSH), and a Lesion Segmentation Head (LeSH) for precise delineation of leaf and lesion areas. The GFPN enhances the traditional Feature Pyramid Network (FPN) through differential sampling and grouping strategies, significantly improving the capture of fine-grained disease characteristics. The MLCH enables simultaneous classification of multiple diseases on a single leaf, effectively addressing the mixed infection problem. The segmentation heads (LSH and LeSH) work in tandem to accurately isolate leaf and lesion regions, facilitating detailed analysis of disease patterns. Experimental results on the Plant Pathology 2021-FGVC8 dataset demonstrate ALMDR’s effectiveness, outperforming state-of-the-art methods across multiple tasks. Our model achieves high performance in multi-label classification (F1-score of 93.74%), detection and segmentation (mean Average Precision (mAP) of 51.32% and 45.50%, respectively), and disease severity estimation ($R^2$ = 0.9757). Additionally, the model maintains this accuracy while processing 6.25 frames per second, balancing performance with computational efficiency. ALMDR demonstrates potential for real-time disease management in apple orchards, with possible applications extending to other crops. Full article

Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture. Full article

Apple leaf diseases can significantly affect the yield and quality of apple crops. However, conventional manual detection methods are inefficient and highly susceptible to subjective judgment, rendering them inadequate for large-scale agricultural production. To address these limitations, this study proposes Mobile-CBSD, a lightweight deep learning model for apple leaf disease detection based on an enhanced version of YOLOv11. First, the original backbone network of YOLOv11 is replaced with MV4_CBAM, a lightweight architecture that improves feature representation capability while reducing model size. Second, the SE attention mechanism is redesigned and integrated into the network to strengthen multi-scale feature fusion. Furthermore, the traditional CIoU loss function is replaced with SIoU to accelerate convergence and enhance localization precision. Experimental results demonstrate that, while maintaining model compactness, Mobile-CBSD achieves an mAP@0.5 of 90.89%, representing a 2.16% improvement over the baseline, along with a 1.02% increase in overall precision. The model size is reduced from 5.4 MB to 4.8 MB. These findings indicate that Mobile-CBSD achieves an effective balance among accuracy, inference speed, and deployability, offering a practical and scalable solution for the efficient monitoring of apple leaf diseases. Full article

Plant diseases have a large impact on agricultural production, leading to crop yield reduction and causing economic losses. For the development of intelligent agriculture, it is very important to identify crop diseases accurately. With the help of image recognition methods, precise prevention and control of diseases can be achieved, which significantly reduces the use of pesticides and ultimately improves crop yield and quality. Therefore, this study proposes a theoretical method that combines Attention-Guided PCA (AG-PCA) dimensionality reduction with a spatial attention mechanism. Our method is verified on the ResNet model. The AG-PCA module dynamically selects principal component features based on attention weights, which greatly preserves key disease features during dimensionality reduction. At the same time, a spatial attention mechanism is embedded in the residual blocks to enhance the representation ability of disease regions and suppress background interference. On the AppleLeaf9 dataset containing 10,211 images of 9 disease categories, the model achieved an accuracy of 93.69%, significantly outperforming the baseline methods. Experimental results indicate that it performs stably in complex backgrounds and fine-grained classification tasks, and demonstrates strong generalization ability, showing promising application potential. Full article

Apple scab poses a significant threat to wild apple orchards in the Ili region of Xinjiang, yet the pathogen responsible and its disease dynamics remain poorly understood. This study aimed to identify the causal agent of apple scab in wild apples and elucidate its disease development pattern to support effective monitoring and control strategies. Field surveys were conducted regularly from 2023 to 2025 in fixed plots and sample trees of Malus sieversii. A total of 29 isolates were obtained from diseased fruits collected in Xinyuan and Huocheng counties using tissue isolation and single-spore purification. Pathogenicity was confirmed via Koch’s postulates, and the pathogen was identified based on morphological and molecular characteristics. Scab symptoms first appeared on leaves in late April (during leaf expansion, disease index 0.34) and on fruits in early June (during fruit enlargement, disease index 0.57). The disease index peaked in late August (47.24 on leaves; 22.51 on fruits), followed by fruit drop at month-end and leaf abscission in late September. The pathogen overwintered mainly in remaining or fallen diseased leaves (isolation rate 17.71%), serving as the primary source of initial infection in the following growing season. The pathogen causing apple scab in Xinjiang wild apple orchards was identified as Venturia inaequalis. Overwintered infected leaves were confirmed as the key primary inoculum source. These findings clarify the taxonomic identity of the pathogen and its epidemic pattern, providing a theoretical basis for disease management. Full article

The leaf diseases of Panax notoginseng (Panax notoginseng (Burk) F. H. Chen) are mainly spread by spores. To enable rapid and sensitive detection of spores for early warning of disease spread, we developed a carbon dot-based fluorescent probe encapsulated by MIL-101 using wax apple as a green carbon source (WA-CDs@MIL-101). The WA-CDs@MIL-101 was thoroughly characterized, and the detection conditions were optimized. The interaction mechanism between WA-CDs@MIL-101 and spores was investigated. The fluorescence of WA-CDs@MIL-101 was recovered due to electrostatic adsorption between spores and WA-CDs@MIL-101. Under the optimized detection conditions, the probe exhibited excellent sensing performance, showing a strong linear relationship (R² = 0.9978) between spore concentration (0.0025–5.0 mg/L) and fluorescence recovery ratio, with a detection limit of 5.15 μg/L. The WA-CDs@MIL-101 was successfully applied to detect spores on Panax notoginseng leaves, achieving satisfactory recoveries (94–102%) with relative standard deviations of 1.3–3.4%. The WA-CDs@MIL-101 shows great promise for detecting spores on Panax notoginseng leaves. Full article

Colletotrichum fructicola is a member of the gloeosporioides complex and can act as a pathogen, causing anthracnose in various plants and as an endophyte residing in healthy plants. As a plant pathogen, C. fructicola has been frequently reported to cause anthracnose in chili fruit and tea plants, bitter rot in apples and pears, crown rot in strawberries, and Glomerella leaf spot in apples, which are the most common diseases associated with this pathogen. Over the years, C. fructicola has been reported to infect a wide range of plants in tropical, subtropical, and temperate regions, including various types of fruit crops, ornamental and medicinal plants, tree nuts, peanuts, and weeds. Several reports have also been made regarding endophytic C. fructicola recovered from different plant parts. Endophytic C. fructicola has the ability to switch to a pathogenic state, which may contribute to the infection of host and other susceptible plants. Due to the economic importance of C. fructicola infections, the present review highlighted C. fructicola as a plant pathogen and endophyte, providing a summary of its infections in various plants and endophytic ability to inhabit plant tissues. Several control measures for managing C. fructicola infections have also been provided. Full article

Accurately identifying apple leaf diseases in complex field environments is a critical concern for intelligent agriculture, as early detection directly affects crop health and yield outcomes. However, accurate feature recognition remains a significant challenge due to the complexity of disease symptoms, background interference, and variations in lesion color and size. In this study, we propose an enhanced detection framework named DMN-YOLO. Specifically, the model integrates a multi-branch auxiliary feature pyramid network (MAFPN), along with Superficial Assisted Fusion (SAF) and Advanced Auxiliary Fusion (AAF) modules, to strengthen feature interaction, retain shallow-layer information, and improve high-level gradient transmission, thereby enhancing multi-scale lesion detection performance. Furthermore, the RepHDWConv module is incorporated into the neck network to increase the model’s representational capacity. To address difficulties in detecting small and overlapping lesions, a lightweight RT-DETR decoder and a dedicated detection layer (P2) are introduced. These enhancements effectively reduce both missed and false detections. Additionally, a normalized Wasserstein distance (NWD) loss function is introduced to mitigate localization errors, particularly for small or overlapping lesions. Experimental results demonstrate that DMN-YOLO achieves a 5.5% gain in precision, a 3.4% increase in recall, and a 5.0% improvement in mAP@50 compared to the baseline, showing consistent superiority across multiple performance metrics. This method offers a promising solution for robust disease monitoring in smart orchard applications. Full article

The accurate and rapid detection of apple leaf diseases is a critical component of precision management in apple orchards. The existing deep-learning-based detection algorithms for apple leaf diseases typically demand high computational resources, which limits their practical applicability in orchard environments. Furthermore, the detection of apple leaf diseases in natural settings faces significant challenges due to the diversity of disease types, the varied morphology of affected areas, and the influence of factors such as lighting variations, leaf occlusions, and differences in disease severity. To address the above challenges, we constructed an apple leaf disease detection (ALD) dataset, which was collected from real-world scenarios, and we applied data augmentation techniques, resulting in a total of 9808 images. Based on the ALD dataset, we proposed a lightweight YOLO11n-based detection network, named CEFW-YOLO, designed to tackle the current issues in apple leaf disease identification. First, we designed a novel channel-wise squeeze convolution (CWSConv), which employs channel compression and standard convolution to reduce computational resource consumption, enhance the detection of small objects, and improve the model’s adaptability to the morphological diversity of apple leaf diseases and complex backgrounds. Second, we developed an enhanced cross-channel attention (ECCAttention) module and integrated it into the C2PSA_ECCAttention module. By extracting global information, combining horizontal and vertical convolutions, and strengthening cross-channel interactions, this module enables the model to more accurately capture disease features on apple leaves, thereby enhancing detection accuracy and robustness. Additionally, we introduced a new fine-grained multi-level linear attention (FMLAttention) module, which utilizes multi-level asymmetric convolutions and linear attention mechanisms to improve the model’s ability to capture fine-grained features and local details critical for disease detection. Finally, we incorporated the Wise-IoU (WIoU) loss function, which enhances the model’s ability to differentiate overlapping targets across multiple scales. A comprehensive evaluation of CEFW-YOLO was conducted, comparing its performance against state-of-the-art (SOTA) models. CEFW-YOLO achieved a 20.6% reduction in computational complexity. Compared to the original YOLO11n, it improved detection precision by 3.7%, with the mAP@0.5 and mAP@0.5:0.95 increasing by 7.6% and 5.2%, respectively. Notably, CEFW-YOLO outperformed advanced SOTA algorithms in apple leaf disease detection, underscoring its practical application potential in real-world orchard scenarios. Full article

Apple (Malus domestica) is an economically important fruit crop, but its production is affected by Glomerella leaf spot, a devastating disease caused by the fungal pathogen Colletotrichum gloeosporioides. MicroRNA (miRNA) is a kind of non-coding RNA that plays an important role in the process of plant–pathogen interactions. However, little is known about the miRNAs that influence apple resistance against C. gloeosporioides. A novel miRNA, MIR396d-p3, was identified through small RNA sequencing (sRNA-seq). Functional analyses revealed that MIR396d-p3 negatively regulates apple resistance to C. gloeosporioides. In addition, MdUGT89A2 and MdRGA3 were confirmed as targets of MIR396d-p3 using 5′ RACE and heterologous expression assays. We further found that overexpressing MdUGT89A2 and MdRGA3 induce apple disease resistance to C. gloeosporioides, while silencing of MdUGT89A2 and MdRGA3 reduces resistance to C. gloeosporioides. These results indicate that MIR396d-p3 plays a role in the response to the infection of C. gloeosporioides through regulating the expressions of MdUGT89A2 and MdRGA3. This research provides a new perspective on the interaction between apples and C. gloeosporioides and offers possible targets for resistance breeding. Full article

As an important and nutrient-rich economic crop, apple is significantly threatened by leaf diseases, which severely impact yield, making the timely and accurate diagnosis and segmentation of these diseases crucial. Traditional segmentation models face challenges such as low segmentation accuracy and excessive model size, limiting their applicability on resource-constrained devices. To address these issues, this study proposes RMP-UNet, an efficient and lightweight model for apple leaf disease segmentation. Based on the traditional UNet architecture, RMP-UNet incorporates an efficient multi-scale attention mechanism (EMA) along with innovative lightweight reparameterization modules (RepECA) and multi-scale feature fusion dynamic upsampling modules (PagDy), optimizing feature extraction and fusion processes to improve segmentation accuracy while reducing model complexity. The experimental results demonstrate that RMP-UNet achieves superior performance compared to mainstream models across multiple metrics, including a mean Intersection over Union (mIoU) of 83.27%, mean pixel accuracy of 89.84%, model size of 9.26 M, and computational complexity of 21.55 G FLOPs, making it suitable for deployment in resource-constrained environments and providing an efficient solution for real-time apple leaf disease diagnosis. Full article

In the realm of apple cultivation, the efficient and real-time monitoring of Gray Leaf Spot is the foundation of the effective management of pest control, reducing pesticide dependence and easing the burden on the environment. Additionally, it promotes the harmonious development of the agricultural economy and ecological balance. However, due to the dense foliage and diverse lesion characteristics, monitoring the disease faces unprecedented technical challenges. This paper proposes a detection model for Gray Leaf Spot on apple, which is based on an enhanced YOLOv8 network. The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. The experimental results show that the enhanced model outperforms the original YOLOv8 network in detecting Gray Leaf Spot. Notably, when the Intersection over Union (IoU) is 0.5, an improvement of 7.92% in average precision is observed. Therefore, this advanced detection technology holds pivotal significance in advancing the sustainable development of the apple industry and environment-friendly agriculture. Full article