Alternaria Solani Research Articles

The development of locally adapted, disease-tolerant and high-yielding semi-determinate tomato hybrids is an emerging focus in agricultural research of Pakistan. These hybrids are designed to address challenges such as disease resistance, particularly against fungal and viral infections, while ensuring high productivity coupled with suitable fruit traits and adaptability to environmental conditions. In Punjab, determinate, indeterminate and semi-determinate tomatoes are cultivated, however, their production is heavily reliant on plant protection measures, making cultivation both costly and largely confined to farmers’ fields. Marjan F1, a semi-determinate tomato hybrid was developed with a fair degree of tolerance against fungal diseases, making it well-suited for open-field cultivation. Its resistance enables cultivation with reduced dependence on plant protection inputs. Additionally, Marjan F1 is highly suitable for kitchen gardening, as it can be cultivated with minimal plant protection measures, thereby allowing home gardeners to grow it organically. In yield trials, Marjan F1 demonstrated a 3.98% yield advantage over three exotic check hybrids (TO-1057 F1, T-1359 F1 and TO-6242 F1). It showed resistance to late blight, gray mold and Fusarium wilt, while maintaining moderate resistance to early blight. Overall, Marjan F1 exhibited superior disease resistance compared to two exotic checks (TO-1057 F1 and TO-6242 F1), coupled with better yield performance.

Genistein: A promising botanical fungicide candidate for enhancing tomato yield and quality by controlling Alternaria solani.

This research mainly focused on with the exploration aming deep learning and utilized ‎through a CNN model, Transfer learning, and integrated with multi-model identification of plant ‎diseases. The main objective is to identify the agronomic flaws in conventional farming ‎practices that led to wasteful and unproductive approaches, including the failure to diagnose ‎malaria, a major health risk that reduced agricultural yields. Using the Plant Village dataset, ‎which includes 54,000 images of plant leaves annotated with disease kind, we were able to ‎construct a better field readiness model by identifying 38 different illnesses using a subset of ‎these photos that provides around 30,000 normal and diseased leaves. We tried a number of ‎different ways to add to the data so that performance would go up and overfitting would be ‎avoided. Accuracy, precision, and recall for illness classes are all above average in our CNN-based model, which achieves 94.9% on average. The confusion matrix revealed only a few ‎misclassified photos, with the best accuracy for healthy leaves (98.0%), followed by "Tomato ‎Early Blight" (94.9%) and "Potato Late Blight" (96.2%). Refined a pretrained EfficientNet ‎model to achieve 96.5% accuracy. Madonna and others. This model can also train up to 25% ‎faster than other designs like ResNet, which makes it good for real-time mobile and edge ‎applications. When the environmental data (temperature, humidity) and pictures of leaves were ‎put together, the accuracy was similarly 97.1%. These approaches worked well together to find ‎illness heterogeneity caused by the environment. This work demonstrates that deep learning ‎‎(DL) may be efficiently employed for early disease detection in plantations, a fundamental ‎aspect of precision agriculture‎.

Risk assessment and underlying mechanisms of pydiflumetofen resistance in Alternaria tenuissima and Alternaria alternata.

Alternaria species are the major pathogens affecting potatoes, yet their pathogenic variability among species and potential to cause disease due to mycotoxins production are not well understood. A total of 48 symptomatic samples (44 leaves and 4 tubers) were collected from potato fields across major cultivation regions of India. From these, 25 representative isolates were selected to represent the full range of pathogenicity (low, moderate, and high virulence) for detailed molecular, morphological, and toxigenic characterization. Morphologically different strains were further subjected to ITS-based phylogenetic analysis, which clustered the isolates into section Alternaria and section Porri. HPLC analysis quantified mycotoxins production in all isolates with tenuazonic acid (TeA) ranging from 30.00-417.81µg/mL and alternariol (AOH) from 20.00-97.01µg/mL. LC-MS/MS with multiple reaction monitoring (MRM) transitions confirmed both compounds in representative isolates. Plate assay of cell wall-degrading enzyme activities indicated that isolates with higher pathogenicity had larger halo diameters, implying higher cellulase and pectinase activity. Hierarchical cluster analysis differentiated isolates based on enzymatic and toxigenic profiles, while correlation analysis showed a high positive correlation between cellulase and pectinase activity (r = 0.62) and moderate correlations between enzyme activity and toxin production (r = 0.48-0.51). These results point towards a synergistic interaction of enzymes and mycotoxins in Alternaria spp. pathogenicity of potato plants. This presents a comprehensive virulence profiling of Alternaria spp. infecting potato plants in India revealed the occurrence of highly virulent Alternaria isolates and highlights the need to monitor newly emerging Alternaria species with high virulence potential in potato production systems.

Iron oxide (FeONP) and copper oxide (CuONP) nanoparticles were synthesized using the leaf extract of Conocarpus lancifolius. Their activity against two phytopathogenic fungi, Alternaria solani and Fusarium solani, was investigated. The colonies’ diameter and morphological changes in the fungal hyphae and conidia treated with nanoparticles were examined using scanning electron microscopy (SEM). CuONPs showed oval particles with a wide particle size distribution of ~57 nm in length and ~28 nm in width. FeONPs showed elliptical disks with a wider particle size distribution of 32 to 39 nm in length and 5 to 14 nm in width. Both types of nanoparticles exhibited significant antifungal activity against A. solani and F. solani. CuONPs inhibited A. solani growth by 88.9% to 91.1% in terms of the fungal colony diameter after 12 days of incubation. They completely inhibited the growth of F. solani. In contrast, FeONPs reduced the growth of A. solani from 68.9% to 73.3%. The SEM images suggest that CuONPs and FeONPs damaged and distorted the fungus structure, consequently limiting and inhibiting fungal growth. Therefore, the green synthesized nanoparticles could be used as antifungal candidates to protect plants against phytopathogenic fungi.

Succinate dehydrogenase inhibitors (SDHIs) are one of the major classes of phytofungicides that inhibit mitochondrial complex II and prevent mitochondrial respiration. Despite their broad applications, only a few SDHIs are available in the market, and rapidly evolving microbes demand the development of new fungicides. In this context, we designed thiazole-carboxamide analogues using structure-guided approaches. The antifungal screening afforded a few potential hits, 22a, with inhibition profiles superior to the comparable Boscalid and Fluxapyroxad across the tested fungi, with substantial selectivity toward Alternaria solani and Pyricularia oryzae. The active compound 22a (S2J-23-04) exhibited significant inhibition of SDH activity (IC50 = 20.01 μM), mycelium inhibition in A. solani (EC50 = 4.49 ± 1.04 μg/mL) and P. oryzae (EC50 = 5.13 ± 1.28 μg/mL), and in vivo activity against A. solani in tomatoes. A slight modification of 22a, i.e., replacing methyl with trifluoromethyl, afforded a superior analogue, 22k (S2J-23-47), with enhanced potency in fungal screening, SDH enzyme, and broad and equipotent mycelium inhibition verified by scanning electron microscopy (SEM). Thus, this study identified a potent selective antifungal lead molecule that can be optimized as a novel phytofungicide.

In recent years, potato scab caused by Streptomyces scabies is aggravating year by year, becoming an industrial problem urgently to be resolved. Screening antagonistic bacteria with good inhibitory effect and wide adaptability is the main measure to realize effective prevention and control of the disease. This study screened three strains of antagonistic bacteria DXT2-4, T2-1 and 21-14 with good inhibitory effect on S. scabies by using plate standoff test, and identified them as Bacillus altitudinis, Bacillus safensis and Bacillus pumilus, respectively, based on morphological characteristics, physiological and biochemical properties, and 16S rRNA gene sequences. DXT2-4, T2-1 and 21-14 showed the pot control efficacy of 68.83%, 48.57%, and 57.14%, respectively. The field control efficacy of the three strains was 59.48%, 34.58% and 51.75% in Hulun Buir, Inner Mongolia Autonomous Region and 55.14%, 36.05%, and 49.05% in Huizhou, Guangdong. The three strains could grow normally in the media with pH 1.0-13.0 and with 1%-11% NaCl, and they had inhibitory effects on Rhizoctonia solani, Verticillium dahliae, Alternaria solani, and Fusarium oxysporum. The indole-3-acetic acid yields of DXT2-4, T2-1, and 21-14 were 2.23, 1.11, and 1.67 mg/L, respectively. DXT2-4 and 21-14 demonstrated strong abilities to solubilize phosphorus. The optimal carbon source, nitrogen source, and inorganic salt for fermentation of strain DXT2-4 were 2% molasses+2% corn starch, 2% soybean meal, and 0.3% MgSO4·7H2O, respectively. These findings suggest the three strains of bacteria can efficiently inhibit the growth of S. scabies and have strong environmental adaptability. Particularly, DXT2-4 has the best effects of inhibiting the disease and promoting plant growth, showing a high development value and broad application prospects, this is of great significance for promoting sustainable potato production and ensuring the environmentally sound utilization of resources.

Identification, mycotoxigenic ability and biosynthesis genes of Alternaria spp. from apples in China.

Tomato (Solanum lycopersicum L.) is among the most economically significant and nutritionally valuable vegetable crops grown globally. However, fungal diseases such as Early Blight caused by Alternaria alternata are a major factor limiting yield and fruit quality in tomato production. This study investigates the biocontrol potential of locally isolated rhizobacterium Pseudomonas yamanorum against A. alternata, the causal agent of early blight in tomato, under both in vitro and in planta conditions. In vitro assays demonstrated significant antifungal activity; in the dual confrontation assay, P. yamanorum (108 CFU/mL) reduced A. alternata mycelial growth by 68.7%, while spore germination was inhibited by 88.7%. In planta trials demonstrated that plants treated with P. yamanorum (107 CFU/mL) alone exhibited the lowest disease severity (2.5). The treatments also significantly enhanced plant growth, with shoot length reaching 45 cm versus 26 cm in infected controls. Biochemical analyses revealed increased catalase (94.84 units mg−1 protein min−1), peroxidase (5.83), and ascorbate peroxidase (67.01) activities in treated plants. Total polyphenol and protein contents also increased (0.81 mg/g and 15.82 mg/g, respectively). Furthermore, P. yamanorum treatments maintained fruit quality parameters such as firmness (3.13), sugar content (6.43 °Brix), and juice yield (55.88%), while reducing malondialdehyde (2.02 µmol/g Dry Weight) and electrical conductivity (0.59 mS/cm). These findings highlight P. yamanorum as a promising biocontrol agent and plant growth-promoting bacteria that improve disease resistance, which can be combined with salicylic acid to further enhance crop vigor and fruit quality under biotic stress.

Succinate dehydrogenase inhibitors (SDHIs) have become one of the fastest-growing categories in the fungicide market and are widely utilized for crop protection in agricultural production. Currently, guided by the imperative of cost reduction and efficiency enhancement, the replacement of biphenyl fragments in SDHIs with cost-effective diphenyl ether fragments has emerged as an innovative strategy for developing novel, highly efficient, and broad-spectrum fungicides. Based on the above structural features, 45 thiazole-5-carboxamide derivatives containing diphenyl ether fragments (potentially targeting fungal SDH) were designed and evaluated for their antifungal effects against Rhizoctonia solani, Sclerotinia sclerotiorum, Alternaria alternata, and Alternaria solani. Notably, the in vitro EC50 value of compound IIIe against R. solani was 0.009 mg/L, exhibiting significantly greater potency than thifluzamide (0.039 mg/L), boscalid (1.849 mg/L), fluxapyroxad (0.049 mg/L), and carboxin (0.146 mg/L), and proving comparable to that of the novel SDHIs fungicide flubeneteram (0.008 mg/L). Concurrently, compound IIIe demonstrated high efficacy in controlling rice sheath blight through detached leaf and pot experiments. Further investigations into fungal SDH inhibition, respiratory suppression, mitochondrial membrane potential detection, molecular docking, cell cytotoxicity, scanning electron microscopy, and transmission electron microscopy analysis confirmed the practical value of compound IIIe as a potential SDHI. The present results provide an indispensable complement for the structural optimization of antifungal leads to the targeting of SDH.

To address the challenges of poor model generalization and suboptimal recognition accuracy stemming from limited and imbalanced sample sizes in tomato leaf disease identification, this study proposes a novel recognition strategy. This approach synergistically combines an enhanced image augmentation method based on generative adversarial networks with a lightweight deep learning model. Initially, an Atrous Spatial Pyramid Pooling (ASPP) module is integrated into the CycleGAN framework. This integration enhances the generator’s capacity to model multi-scale pathological lesion features, thereby significantly improving the diversity and realism of synthesized images. Subsequently, the Convolutional Block Attention Module (CBAM), incorporating both channel and spatial attention mechanisms, is embedded into the MobileNetV4 architecture. This enhancement boosts the model’s ability to focus on critical disease regions. Experimental results demonstrate that the proposed ASPP-CycleGAN significantly outperforms the original CycleGAN across multiple disease image generation tasks. Furthermore, the developed CBAM-MobileNetV4 model achieves a remarkable average recognition accuracy exceeding 97% for common tomato diseases, including early blight, late blight, and mosaic disease, representing a 1.86% improvement over the baseline MobileNetV4. The findings indicate that the proposed method offers exceptional data augmentation capabilities and classification performance under small-sample learning conditions, providing an effective technical foundation for the intelligent identification and control of tomato leaf diseases.

Early blight of potato, caused by the fungal pathogen Alternaria solani, is a significant disease that leads to economic losses in potato production across the United States. The disease is characterized by dark brown lesions with concentric rings that primarily affect the leaves but also occur on petioles and stems. This report evaluates the efficacy of the biological control product LifeGard ( Bacillus mycoides isolate J) in managing potato early blight. The field trial was conducted in 2024 using the cultivar ‘Caribou Russet’ potato ( Solanum tuberosum) in Presque Isle, ME. The findings from this study aim to provide practical recommendations for early blight management to potato growers in the United States.

Potato production plays a vital role in global agriculture as a major food source for large populations. However, potato crops are highly susceptible to diseases, particularly Early Blight and Late Blight, which result in substantial yield losses. Timely detection and effective control of these diseases are essential for maintaining stable crop output. This study explores the integration of Convolutional Neural Networks (CNNs) and advanced image processing techniques to differentiate between diseased and healthy potato plants accurately. Two datasets comprising original and enhanced images were used to train four CNN models: InceptionV3, Xception, Densenet201, and Resnet152V2. The original images underwent background removal only, whereas the enhanced images were further processed using contrast enhancement and morphological transformation in addition to background removal to reduce noise, improve quality, and prepare the images for analysis. The CNN models were trained using these datasets, with their bottom layers fixed and the top layers fine-tuned to improve performance and reduce training time. Experimental results revealed that models trained on enhanced images achieved a 2.45% to 4.45% improvement in accuracy, precision, and sensitivity compared to those trained on original images. Moreover, a hybrid model that combined two high- performing CNNs achieved a 98.91% accuracy, marking up to 10.69% improvement over individual models. This approach offers significant potential for reducing crop yield losses while minimizing dependence on chemical treatments.

Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to prioritize symptomatic regions while utilizing depthwise decomposition to reduce parameters to only 104,043 (multiple times smaller than MobileNet and EfficientNet). Evaluated on ten tomato disease classes from PlantVillage, EDANet achieves 97.32% accuracy and exceptional (~1.00) micro-AUC, with perfect recognition of Mosaic virus (100% F1-score) and robust performance on challenging cases like Early blight (93.2% F1) and Target Spot (93.6% F1). The architecture processes 128×128 RGB images in ~23ms on standard CPUs, enabling real-time field diagnostics without GPU dependencies. This work bridges laboratory AI and practical farm deployment by optimizing the accuracy-efficiency tradeoff, providing farmers with an accessible tool for early disease intervention in resource-limited environments.

An unprecedented alternariol-zinnol hybrid and six undescribed zinnol derivatives from the phytopathogen Alternaria solani and their cytotoxic activities.

Assessment of resistance risk and molecular basis of bixafen in Alternaria alternata causing early blight of potato in China

Tomatoes (Solanum lycopersicumL.) play a key role in ensuring food security and are an essential component of the human diet due to their rich content of vitamins and minerals. However, the stability of their production is seriously threatened by phytopathogens, especially representatives of the genus Alternaria, which cause early blight. This study provides a comparative characterization of two isolates -Alternaria alternataand Alternaria tenuissima, based on indicators of enzymatic activity and pathogenicity. The research covered five types of hydrolytic enzymes that degrade the plant cell wall: amylases, proteases, pectinases, phosphatases, and cellulases. The assessment was carried out on days 5 and 7 using indicator media to detect zones of enzymatic activity, as well as by visually measuring colony growth and degradation zones. Both isolates demonstrated moderate to moderately high levels of enzymatic activity, indicating a broad biochemical potential. When comparing enzymatic activity, it was found that both isolates exhibited the highest activity against amylolytic enzymes, with an averagevalue of 1.243, while phosphatase activity was the lowest, with an average index of 1.073. Pathogenicity test results confirmed the ability of both isolates to infect internal and external plant tissues, with no statistically significant differences between them. Nevertheless, A. tenuissimashowed more aggressive external activity, which may be critical for the spread of infection in agricultural ecosystems. The obtained data emphasize the importance of comprehensive assessment of phytopathogens in terms of both aggressiveness and biochemical activity, which is essential for the selection of resistant cultivars and breeding lines, as well as for optimizing biological protection strategies.

Introduction: This research aims to develop an advanced deep learning (DL) model for the accurate detection of potato leaf diseases, specifically Early Blight and Late Blight, which significantly affect crop yield in India. By modifying the DenseNet architecture, the proposed model achieves an accuracy of 99.54%, surpassing previous benchmarks. A mobile app has also been introduced to assist farmers with real-time disease diagnosis, actionable solutions, and expert consultation, thereby improving overall crop management and food security. Artificial Intelligence (AI) and deep learning provide promising solutions for accurate and efficient disease detection. Methods: This study proposes a novel deep learning approach that modifies the DenseNet architecture for improved disease classification. The model was trained on a dataset of potato leaf images, leveraging image processing and deep learning techniques to enhance detection accuracy. Results: The modified Dense-Net model achieved a classification accuracy of 99.54%, outperforming previous existing literature. This significant improvement underscores the robustness and reliability of the proposed approach in effectively detecting potato plant diseases. Discussions: The proposed model achieves a high accuracy of 99.54%, surpassing previous approaches and demonstrating significant advancement in potato disease detection. Its integration into a mobile app bridges the gap between AI research and practical farming applications, offering real-time diagnosis and expert support. However, limitations such as dataset variability and the need for broader field validation remain, highlighting the importance of continuous model updates and testing. Conclusion: To aid farmers, a user-friendly mobile application powered by a deep learning model was developed. The app provides disease diagnosis, treatment recommendations, and real- time consultations with certified agronomists. This AI-driven tool enhances accessibility and decision-making for farmers, promoting sustainable agricultural practices.

Inspired by natural quinoline, a series of novel quinoline-4-ester derivatives featuring a biphenyl motif were designed and synthesized via six steps. All the target compounds were characterized by 1H NMR, 13C NMR, and HRMS. These compounds were evaluated for their fungicidal activity in vitro. The fungicidal activity results showed that all 13 compounds in this series had certain fungicidal activity against 10 types of fungus. Compounds 7a-7c possessed moderate fungicidal activity (31.3%) against Alternaria solani, whereas compound 7g exhibited moderate fungicidal activity (33.3%) against Physalospora piricola. Structure-activity relationships indicated ortho- or meta-methyl substituents will increase activity. Furthermore, the compound 7d was characterized by x-ray diffraction. Hirshfeld surface analysis identified that H···H (46.9%), H···C (11.7%), and H···X (7.3%) interactions are the dominant crystal packing force, whereas density functional theory (DFT) calculations provided the energy gap (ΔE=4.15eV), chemical softness (σ, 0.48) and chemical hardness (η, 2.07), and electrophilicity index (ω, 7.16) for further optimization.