Leaf Disease Severity Research Articles

Cultivar Mixtures as Part of Integrated Protection of Winter Barley from Leaf Diseases and Abiotic Stresses

Background: Currently, cultivar mixtures are being extensively used in agriculture worldwide. The current research study aimed to identify the optimal proportion of cultivars with different resistance to pathogens to reduce the severity of leaf diseases in mixed cultivars against net blotch and leaf rust of barley.Methods: Leaf rust severity was assessed using a modified Cobb scale, and net blotch severity with the E. E. Geshele scale. Disease severity was averaged from triplicate measurements on 25–30 plants. After harvest, 1000 seeds per plot were weighed. Differences were evaluated using the Fisher test (α = 0.05), and correlations via the Chaddock scale in Statistica 13.3.Results: A high degree of inverse correlation was established between the proportion of resistant cultivars and the severity by both diseases r= – 0.93/– 0.99. The optimal mixture combination of a resistant and susceptible cultivar against net blotch was recorded as 1S:4R, and leaf rust as 1S:3R and 1S:4R.Conclusion: In mixed barley crops, a strong inverse correlation was found between the proportion of resistant cultivars and disease severity (r = –0.93/–0.99). Disease severity correlated directly with humidity and temperature in spring, with an inverse correlation for net blotch in June. Using mixed cultivars enhances crop resistance to abiotic and biotic stresses, making it suitable for eco-friendly farming.Keywords: Abiotic factors; Barley; Mixture of cultivars; Puccinia hordei; Pyrenophora teres

A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net

Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.

Histopathological Investigation of Varietal Responses to Cercospora beticola Infection Process on Sugar Beet Leaves.

Cercospora leaf spot (CLS) is the most destructive foliar disease in sugar beet (Beta vulgaris). It is caused by Cercospora beticola Sacc., a fungal pathogen that produces toxins and enzymes which affect membrane permeability and cause cell death during infection. In spite of its importance, little is known about the initial stages of leaf infection by C. beticola. Therefore, we investigated the progression of C. beticola on leaf tissues of susceptible and resistant sugar beet varieties at 12-h intervals during the first 5 days after inoculation using confocal microscopy. Inoculated leaf samples were collected and stored in DAB (3,3'-diaminobenzidine) solution until processed. Samples were stained with Alexa Fluor-488-WGA dye to visualize fungal structures. Fungal biomass accumulation, reactive oxygen species (ROS) production, and the area under the disease progress curve were evaluated and compared. ROS production was not detected on any variety before 36 h postinoculation (hpi). C. beticola biomass accumulation, percentage leaf cell death, and disease severity were all significantly greater in the susceptible variety compared with the resistant variety (P < 0.05). Conidia penetrated directly through stomata between 48 to 60 hpi and produced appressoria on stomatal guard cells at 60 to 72 hpi in susceptible and resistant varieties, respectively. Penetration of hyphae inside the parenchymatous tissues varied in accordance with time postinoculation and varietal genotypes. Overall, this study provides a detailed account to date of events leading to CLS disease development in two contrasting varieties.

An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction

An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction

An intelligent convolution based graph cut segmentation for potato leaf disease severity prediction

An intelligent convolution based graph cut segmentation for potato leaf disease severity prediction

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.

Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production. In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered. Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%. The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value &lt; 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants.

Beech Leaf Disease Severity Affects Ectomycorrhizal Colonization and Fungal Taxa Composition.

Beech leaf disease (BLD) is an emerging forest infestation affecting beech trees (Fagus spp.) in the midwestern and northeastern United States and southeastern Canada. BLD is attributed to the newly recognized nematode Litylenchus crenatae subsp. mccannii. First described in Lake County, Ohio, BLD leads to the disfigurement of leaves, canopy loss, and eventual tree mortality. Canopy loss limits photosynthetic capacity, likely impacting tree allocation to belowground carbon storage. Ectomycorrhizal fungi are root symbionts, which rely on the photosynthesis of autotrophs for nutrition and growth. Because BLD limits tree photosynthetic capacity, ECM fungi may receive less carbohydrates when associating with severely affected trees compared with trees without BLD symptoms. We sampled root fragments from cultivated F. grandifolia sourced from two provenances (Michigan and Maine) at two timepoints (fall 2020 and spring 2021) to test whether BLD symptom severity alters colonization by ectomycorrhizal fungi and fungal community composition. The studied trees are part of a long-term beech bark disease resistance plantation at the Holden Arboretum. We sampled from replicates across three levels of BLD symptom severity and compared fungal colonization via visual scoring of ectomycorrhizal root tip abundance. Effects of BLD on fungal communities were determined through high-throughput sequencing. We found that ectomycorrhizal root tip abundance was significantly reduced on the roots of individuals of the poor canopy condition resulting from BLD, but only in the fall 2020 collection. We found significantly more ectomycorrhizal root tips from root fragments collected in fall 2020 than in spring 2021, suggesting a seasonal effect. Community composition of ectomycorrhizal fungi was not impacted by tree condition but did vary between provenances. We found significant species level responses of ectomycorrhizal fungi between levels of both provenance and tree condition. Of the taxa analyzed, two zOTUs had significantly lower abundance in high-symptomatology trees compared with low-symptomatology trees. These results provide the first indication of a belowground effect of BLD on ectomycorrhizal fungi and contribute further evidence to the role of these root symbionts in studies of tree disease and forest pathology.

Endogenous Salicylic acid Estimation in Wheat leaves treated with Salicylic acid and infected with Alternaria triticina

This study was aime to measure the accumulation of endogenous salicylic acid, as indicator for systemic acquired re-sistance of wheat plants of “Utique” variety. The plant was sprayed with salicylic acid (SA) or water as control, at the five-leaf stage, later infected with leaf blight caused by Alternaria triticina. Leaf samples were removed after 10, 20, and 30 days of inoculation to test their endogenous content of salicylic acid as it is the primary internal signal indicating the emergence of systemic acquired resistance in plants, by the spectrophotometer measuring. Endogenous SA values were ascending dramatically from 10 days to 20 days and maximum with 30th day significantly, while control plants exhibit lower values in all day periods, insignificantly. SA treatments proved reduction in disease incidence after 10 days with 54% and after 20 days with 64%, while after 30 days the reduction recorded high percentage of 80%. In comparison between the time intervals, disease severity was clearly reduction and reached to 83% after 10 days of inoculation then decreased to 72% for both 20th and maintained its stability on the 30th day of inoculation by 72%. This study was proved the reduction of wheat leaf blight incidence and disease severity as a result of treatment by using 1Millimolar (mM) of SA was leading to accumulation of significant levels of endogenous SA as indicator for internal induced resistance in plant.

Leaf disease severity classification with explainable artificial intelligence using transformer networks

Leaf disease severity classification with explainable artificial intelligence using transformer networks

Diagnosis and Mobile Application of Apple Leaf Disease Degree Based on a Small-Sample Dataset.

The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP) was proposed to achieve effective extraction of apple leaf lesion features and to improve the apple leaf disease recognition and disease severity diagnosis compared with the classical semantic segmentation network models PSPNet and GCNet. In addition, the effects of the learning rate, optimizer, and backbone network on the performance of the DeepLabV3+ network model with the best performance were analyzed. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the model reached 97.26% and 83.85%, respectively. After being deployed into the smartphone platform, the detection time of the detection system was 9s per image for the portable and intelligent diagnostics of apple leaf diseases. The transfer learning method provided the possibility of quickly acquiring a high-performance model under the condition of small datasets. The research results can provide a precise guide for the prevention and precise control of apple diseases in fields.

Soil drenching and foliar spray with bioagents for reducing wheat leaf diseases under natural field conditions

Foliar diseases, like powdery mildew, septoria leaf blotch (SLB) and stem rust, affect wheat plants causing severe yield losses. Biofertilizers Pseudomonas fluorescens Migula (NRC2041) and Bacillus subtilis Ehrenberg (NRC313) or the arbuscular mycorrhizal fungi Glomus mosseae Tul. & C. Tul. (NRC212A) and G. fasciculatum Tul. & C. Tul. (NRC212B) were used for soil treatment followed by foliar spray with Trichoderma harzianum or P. fluorescens to manage severity of wheat foliar diseases under field conditions for two successive growing seasons. Under field conditions, at all soil drench and foliar application treatments, Sids 14 was the most sensitive cultivar for high disease severity followed by Misr 2 and Giza 168. Also, the obtained results showed that the most effective treatments were soil drench with mycorrhizae followed by foliar spray with either T. harzianum or P. fluorescens. Meanwhile, the fungicide Amistar showed a superior reduction in disease severity compared to other applied treatments. In addition, the increased yield was correlated to the decreased severity of the recorded diseases. The present study suggests using bioagents for reducing the severity of wheat leaf diseases and increasing the grain yield under field conditions.

MULTINET: A Multi-Agent DRL and EfficientNet Assisted Framework for 3D Plant Leaf Disease Identification and Severity Quantification

MULTINET: A Multi-Agent DRL and EfficientNet Assisted Framework for 3D Plant Leaf Disease Identification and Severity Quantification

The Determination of Tolerances of Some Cotton Genotypes Against Wilt Disease (Verticillium dahliae Kleb.) Under The Ecological Conditions of Diyarbakir

Verticillium wilt disease in cotton is a soil-borne disease that causes significant yield losses, and it is therefore important to identify disease-resistant genotypes. Current study was carried out in the experimental field of the Faculty of Agriculture, Dicle University (Diyarbakir) in the year 2020 to determine the tolerance levels of some cotton genotypes which contain different characteristics to wilt disease (Verticillium dahliae Kleb.). The experiment area has been naturally infected with Verticillium wilt pathogens. A total of 124 cotton genotypes, including four controls (DP-499, Edessa, ST-468, ES-1), were arranged in five blocks according to the Augmented Design. The indices of stem cross-section and the leaf-disease severity were found significant. The leaf-disease severity varied between 0.12-3.09 at 50-60 % at boll opening period, whilst stem cross-section values ranged between 0.36-2.30. Moreover, a positive correlation was found between the indices. In conclusion, G7, G12, G21, G22, G23, G41, G51, G55, and G101 were determined as tolerant genotypes to Verticillium wilt in terms of examining two parameters.

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

SummaryThe earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak‐based UNet (BLSNet), multilayer maxout, resistance spot welding‐based deep recurrent neural network (RSW‐based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

Polysaccharides combined to copper and magnesium improve tomato growth, yield, anti-oxidant and plant defense enzymes

The search for sustainable technologies to increase yield are fundamental for a modern agriculture. Polymers of biological origin (e.g., polysaccharides) have been studied for their potential use in agriculture. These substances present a great possibility of agricultural uses allowing their combination with nutrients to improve the uptake by plants. In this regard, the association of copper and magnesium to polysaccharides was investigated in the tomato cultivation in pots conducted with fertigation under protected environment. Two experiments were conducted: (i) foliar application of polysaccharides and their association with copper and magnesium in the initial growth of tomato plants; and (ii) tomato yield with foliar application of polysaccharides and their association with copper and magnesium. Growth, leaf nutrient content, enzymes and biochemical variables, fruit yield and leaf disease severity index were analyzed. The foliar sprays with polysaccharides in the combination with copper and magnesium was efficient to promote plant growth and yield. It also improved the concentration of antioxidant enzymes and the β,1,3 glucanase enzyme, while reducing the severity of powdery mildew in tomato leaves.

The effect of nitrogen fertilization on yield and macronutrient concentrations in root chicory (Cichorium intybus L. var. Sativus Bisch) and the health status of plants

The effect of N fertilization on the health status of chicory plants, yield and the content of dry matter and macronutrients in chicory roots was determined in the study. Three root chicory cultivars, Polanowicka, Orchies and Chrysolite, were grown in a plot experiment. Three levels of topsoil N fertilization were applied before sowing: 0, 80 and 120 kg ha–1. The severity of leaf diseases was estimated during the growing season. Root yield, agronomic and marginal N-use efficiency, DM content and macronutrient concentrations in roots were calculated after harvest (10–20 October). The symptoms of powdery mildew, gray mold and leaf spot on chicory leaves were significantly least severe in the unfertilized treatment. The highest yield (83 Mg ha–1) was obtained in 2017, in cv. Chrysolite without N fertilization. Root yield decreased in response to the application of N fertilizer at both rates. A minor increase in yield was observed only in cv. Polanowicka in N-fertilized treatments in 2017, and in cv. Chrysolite fertilized with 80 kg N ha–1 in 2018. Root yield was negatively correlated with disease severity during the growing season. The DM content of chicory roots (mean values for years of the study, cultivars and N rates) was similar in all treatments. N fertilization induced changes in the content of N, K, Mg and S in chicory roots of the analyzed cultivars. The application of N fertilizer had a beneficial influence on the N content of roots in all cultivars.

Higher fitness and competitive advantage of Pyricularia oryzae Triticum lineage resistant to QoI fungicides.

Quinone outside inhibitor (QoI) fungicides have not been effective in controlling the wheat blast disease [Pyricularia oryzae Triticum lineage (PoTl)] in Brazil. The first report of resistance of PoTl to QoIs in this country occurred in 2015. This study aimed to test hypotheses about the changes in fitness parameters and competitive advantage of the QoI-resistant (R) PoTl isolate group compared to the sensitive (S) isolate group. Mycelial growth on PDA medium and in vivo conidial production, incubation period and disease severity were analyzed as fitness parameters. The competitive ability was measured on wheat leaves and heads inoculated with mixtures of R:S isolates at the following proportions: 0S:100R, 20S:80R, 50S:50R, 80S:20R, 100S:0R, and 0S:0R. The QoI-R isolate group had significantly higher fitness than the sensitive isolate group, considering both in vitro and in vivo parameters. The highest in vivo conidial production on wheat leaves and the highest leaf and head disease severity were detected when resistant strains were predominant in the isolate's mixtures (20S:80R or 0S:100R proportions), in the absence of fungicide pressure. Conidia harvested from wheat blast lesions on leaves inoculated with 20S:80R and 0S:100R mixtures were resistant to QoIs in vitro assays based on discriminatory doses of the fungicide. Therefore, QoI resistance facilitated a higher fitness and a competitive advantage in PoTl, which contrasts with the evolutionary theory that associates a fitness cost to fungicide resistance. We discuss the evolutionary and ecological implications of the higher fitness as found in the fungicide-resistant adapted populations of the wheat blast pathogen. © 2022 Society of Chemical Industry.

Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation

Convolutional neural networks (CNNs) are the gold standard in the machine learning (ML) community. As a result, most of the recent studies have relied on CNNs, which have achieved higher accuracies compared with traditional machine learning approaches. From prior research, we learned that multi-class image classification models can solve leaf disease identification problems, and multi-label image classification models can solve leaf disease quantification problems (severity analysis). Historically, maize leaf disease severity analysis or quantification has always relied on domain knowledge—that is, experts evaluate the images and train the CNN models based on their knowledge. Here, we propose a unique system that achieves the same objective while excluding input from specialists. This avoids bias and does not rely on a “human in the loop model” for disease quantification. The advantages of the proposed system are many. Notably, the conventional system of maize leaf disease quantification is labor intensive, time-consuming and prone to errors since it lacks standardized diagnosis guidelines. In this work, we present an approach to quantify maize leaf disease based on adaptive thresholding. The experimental work of our study is in three parts. First, we train a wide variety of well-known deep learning models for maize leaf disease classification, then we compare the performance of the deep learning models and finally extract the class activation heatmaps from the prediction layers of the CNN models. Second, we develop an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge. Lastly, we use these regions of interest to estimate image leaf disease severity. Experimental results show that transfer learning approaches can classify maize leaf diseases with up to 99% accuracy. With a high quantification accuracy, our proposed adaptive thresholding method for CNN class activation maps can be a valuable contribution to quantifying maize leaf diseases without relying on domain knowledge.

Phytopathogenicity and control of Emmia lacerata FMIB29 on tomato (Solanum lycopersicum L.) plant

Tomato (Solanum lycopersicum L.) production is constantly threatened by wide array of mycopathogens. Emmia lacerata has been investigated as an endophyte, a saprophyte, a respiratory pathogen, and presently, as a soil-borne mycopathogen of tomato. The disease caused by E. lacerata FMIB29 in tomato was characterised through phenotypic assessment and controlled with antifungal plant extracts, using soil amendment assay (0.2 g/plant). Pathogenesis of E. lacerata on tomato ranged from mild chlorosis to severe stunt, or plant death. The highest leaf number, plant height, and disease severity reduction of 17.33 cm, 39.00 and 43.80%, respectively were recorded on G. arborea treated plants. Aqueous extracts of these inhibitory plants are accessible, and could be further studied for their biocontrol potentials against mycopathogens like E. lacerata. This will encourage tomato production and prevent pathogen transfer to humans; essentially, to avoid complications in individuals suffering from COVID-19 and other respiratory diseases.