scholarly journals Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery

2021 ◽  
Vol 12 ◽  
Author(s):  
Da-Young Lee ◽  
Dong-Yeop Na ◽  
Carlos Góngora-Canul ◽  
Sriram Baireddy ◽  
Brenden Lane ◽  
...  
Keyword(s):  
Disease Severity ◽  
High Throughput ◽  
Traditional Approach ◽  
Weighted Kappa ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Image Block ◽  
Progress Curve ◽  
Tar Spot ◽  
Rgb Images

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.

scholarly journals A generalised approach for high-throughput instance segmentation of stomata in microscope images

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiranya Jayakody ◽  
Paul Petrie ◽  
Hugo Jan de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

scholarly journals Epidemiology and management of bacterial leaf spot on watermelon caused byPseudomonas syringae

Plant Disease ◽  
2017 ◽  
Vol 101 (7) ◽  
pp. 1222-1229 ◽  
Author(s):  
E. A. Newberry ◽  
L. Ritchie ◽  
B. Babu ◽  
T. Sanchez ◽  
K. A. Beckham ◽  
...  
Keyword(s):  
Disease Severity ◽  
Pseudomonas Syringae ◽  
Leaf Spot ◽  
Negative Relationship ◽  
Disease Outbreaks ◽  
Field Trials ◽  
Multiple Regression Model ◽  
Bacterial Leaf Spot ◽  
Progress Curve ◽  
Cool Conditions

Bacterial leaf spot of watermelon caused by Pseudomonas syringae has been an emerging disease in the southeastern United States in recent years. Disease outbreaks in Florida were widespread from 2013 to 2014 and resulted in foliar blighting at the early stages of the crop and transplant losses. We conducted a series of field trials at two locations over the course of two years to examine the chemical control options that may be effective in management of this disease, and to investigate the environmental conditions conducive for bacterial leaf spot development. Weekly applications of acibenzolar-S-methyl (ASM) foliar, ASM drip, or copper hydroxide mixed with ethylene bis-dithiocarbamate were effective in reducing the standardized area under the disease progress curve (P < 0.05). Pearson’s correlation test demonstrated a negative relationship between the average weekly temperature and disease severity (–0.77, P = 0.0002). When incorporated into a multiple regression model with the square root transformed average weekly rainfall, these two variables accounted for 71% of the variability observed in the weekly disease severity (P < 0.0001). This information should be considered when choosing the planting date for watermelon seedlings as the cool conditions often encountered early in the spring season are conducive for bacterial leaf spot development.

Impact of weather parameters on alternaria blight of Indian mustard [(Brassica juncea (L.) Czern. & Coss.)]

2021 ◽  
Vol 50 (1) ◽  
pp. 15-19
Author(s):  
Rakesh Punia ◽  
Pavitra Kumari ◽  
Anil Kumar ◽  
AS Rathi ◽  
Ram Avtar
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Disease Severity ◽  
Vapour Pressure ◽  
Minimum Temperature ◽  
Indian Mustard ◽  
Maximum Increase ◽  
Alternaria Blight ◽  
Progress Curve ◽  
Sunshine Hours

Progression of Alternaria blight disease was measured on two susceptible Indian mustard varieties viz., RH 30 and RH 0749 sown at three different dates. The maximum increase in disease severity was recorded between first weeks of February and last week of February. During this period, the maximum and minimum temperature, relative humidity at morning and evening, average vapour pressure of morning and evening, maximum and bright sunshine hours and wind speed were higher, which resulted in congenial conditions for severe infection by the pathogen. The disease severity was positively correlated with maximum and minimum temperature, average vapour pressure, wind speed, sunshine hours and evaporation, while relative humidity and rainfall negatively correlated with Alternaria blight on both the varieties. A maximum value of area under disease progress curve was observed on cultivar RH 30 (651.1 cm2) as compared to RH 0749 (578.9 cm2), when crop was sown on 9th November.

scholarly journals Management of Daylily Rust With Different Fungicides and Fungicide Combinations

2017 ◽  
Vol 18 (3) ◽  
pp. 162-165 ◽  
Author(s):  
Robert S. Emmitt ◽  
James W. Buck
Keyword(s):  
United States ◽  
Disease Management ◽  
Disease Severity ◽  
Low Cost ◽  
Single Application ◽  
Southeast United States ◽  
Disease Progress Curve ◽  
Disease Progress ◽  
Progress Curve ◽  
Disease Pressure

Production nurseries and daylily hybridizers in the southeast United States rely on the use of fungicides to manage daylily rust, caused by the fungus Puccinia hemerocallidis. Foliar sprays of pyraclostrobin, flutolanil, tebuconazole, myclobutanil, chlorothalonil, mancozeb, pyraclostrobin + boscalid, flutolanil + tebuconazole, flutolanil + myclobutanil, flutolanil + chlorothalonil, and flutolanil + mancozeb applied on 14-day intervals, and a nontreated control, were evaluated under high disease pressure at three locations in Griffin, GA, in 2015. Tebuconazole or the tebuconazole + flutolanil treatment consistently had the lowest area under the disease progress curve (AUDPC) of the treatments. The addition of flutolanil to chlorothalonil or mancozeb did not improve rust control and no difference in disease severity was observed in any treatment containing contact fungicides on all assessment dates. Single application costs ranged from $10.21 to $95.96 with tebuconazole providing excellent disease management at a relatively low cost per application ($13.90).

scholarly journals Cotton resistance to ramulose and variability of Colletotrichum gossypii f. sp. cephalosporioides

2006 ◽  
Vol 32 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Jefferson Fernandes do Nascimento ◽  
Laércio Zambolim ◽  
Francisco Xavier Ribeiro do Vale ◽  
Paulo Geraldo Berger ◽  
Paulo Roberto Cecon
Keyword(s):  
Disease Severity ◽  
Disease Incidence ◽  
Disease Index ◽  
Mato Grosso ◽  
Progress Curve ◽  
Commercial Cultivars ◽  
Resistant Group ◽  
The Relationship ◽  
Highly Virulent ◽  
Susceptible Group

Four cultivars and 21 lines of cotton were evaluated for resistance to ramulose (Colletotrichum gossypii f. sp. cephalosporioides) in a field where the disease is endemic. The seeds of each genotype were planted in 5 x 5 m plots with three replications. The lines CNPA 94-101 and 'CNPA Precoce 2'were used as standard susceptible and resistant references, respectively. The disease incidence (DI) was calculated from the proportion of diseased plants in the plot. The disease index (DIn) was calculated from the disease severity using a 1 to 9 scale, and was evaluated at weekly intervals starting 107 days after emergence. The data collected was used to calculate the area under disease progress curve (AUDPC). In general, the DIn increased linearly with time and varied from 20.0 to 57.1 and AUDPC from 567 to 1627 among the genotypes which could be clustered in to two distinct groups. The susceptible group contained two cultivars and nine lines and the resistant group contained one cultivar and 12 lines. The relationship between disease index and evaluation times was linear for the 25 genotypes tested. The line CNPA 94-101, used as susceptible standard, was the most susceptible with an average DI = 83.4, DIn = 57.1 and AUDPC = 1627.7. The line CNPA 96-08 with DI = 37.8, DIn = 20.0 and AUDPC = 567.7 was the most resistant one. Among the commercial cultivars 'IAC 22' was the most susceptible and 'CNPA Precoce 2', used as resistant standard was the most resistant. The variability in virulence of the pathogen was studied by spray inoculating nine genotypes with conidial suspensions (10(5)/mL) of either of the 10 isolates. The disease severity was evaluated 30 days later using a scale of 1 to 5. The virulence of the isolate was expressed by DIn. All the isolates were highly virulent but their virulence avaried for several genotypes and could be clustered in two distinct groups of less and more virulent isolates. The isolate MTRM 14 from Mato Grosso was the least virulent while Minas Gerais was the most virulent, with DIn of 6.36 and 46.47, respectively. In this experiment the line HR 102 and the cultivar 'Antares' were the most resistant ones with DIns of 18.32 and 19.14, respectively.

scholarly journals Identification of Bone Fragmentation in X-Ray Images using Contour Detection Algorithm

2019 ◽  
Vol 8 (9) ◽  
pp. 2121-2124
Keyword(s):  
Edge Detection ◽  
Bone Fracture ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Crack Identification ◽  
Fracture Detection ◽  
Canny Edge Detection ◽  
X Ray ◽  
Base Paper ◽  
Canny Edge

The crack can occur in any bone ofour body. Broken bone is a bone condition that endured a breakdown of bone trustworthiness. The Fracture can't recognize effortlessly by the bare eye, so it is found in the x-beam images. The motivation behind this task is to find the precise territory where the bone fracture happens utilizing X-Ray Bone Fracture Detection by Canny Edge Detection Method. Shrewd Edge Detection technique is an ideal edge identification calculation on deciding the finish of a line with alterable limit and less error rate. The reproduction results have indicated how canny edge detection can help decide area of breaks in x-beam images. In the base paper, the cracked bit is chosen physically to defeat this downside, the proposed technique identify the bone fracture consequently and furthermore it quantifies the parameter like length of the crack, profundity of the fracture and the situation of the crack as for even and vertical pivot. The outcome demonstrates that the proposed technique for crack identification is better. The outcomes demonstrate that calculation is 91% exact and effective

scholarly journals Alternative Products to Control Soybean Downy Mildew in the Field

2020 ◽  
Vol 12 (8) ◽  
pp. 160
Author(s):  
Gislaine Gabardo ◽  
Maristella Dalla Pria ◽  
Henrique Luis da Silva ◽  
Mônica Gabrielle Harms
Keyword(s):  
Downy Mildew ◽  
Disease Severity ◽  
Field Experiments ◽  
Ascophyllum Nodosum ◽  
Phenological Stages ◽  
Manganese Zinc ◽  
Growing Seasons ◽  
Progress Curve ◽  
Copper Manganese ◽  
Diagrammatic Scale

Soybean mildew caused by Oomycota Peronospora manshurica, is a disease widely spread in Brazil. In order to study the efficiency of soybean mildew control due to the application of alternative products and fungicide in the field, experiments were conducted in Ponta Grossa, PR, Brazil, during the 2013/2014 and 2014/2015 growing seasons. The design used was randomized blocks with four replications. The treatments were: 1-witness; 2-acibenzolar-S-methyl; 3-calcium; 4-micronutrients: copper, manganese and zinc; 5-micronutrients: manganese, zinc and molybdenum; 6-NK fertilizer; 7-Ascophyllum nodosum and 8-azoxystrobin + cyproconazole with the addition of Nimbus adjuvant. Four applications of alternative products (phenological stages V3, V6, R1 and R5.1) and two of fungicide (phenological stages R1 and R5.1) were performed. The mildew severity was estimated using a diagrammatic scale. The severity data made it possible to calculate the area under the disease progress curve (AUDPC). In the 2014/2015 harvest the disease was more severe. The control of downy mildew by the use of fungicide did not reduce the epidemic. The fungicide was not efficient in the two evaluated seasons. All tested alternative products reduced the disease severity and AUDPC in both seasons. The best results in reducing downy mildew were found with the application of acibenzolar-S-methyl, micronutrients (Cu, Mn, Zn) and A. nodosum.

Comparative Performance of Sanitizers in Managing Plant-to-plant Transfer and Postharvest Infection of Calonectria pseudonaviculata and Pseudonectria foliicola on Boxwood

Plant Disease ◽  
2021 ◽  
Author(s):  
Ravi Bika ◽  
Warren Copes ◽  
Fulya Baysal-Gurel
Keyword(s):  
Shelf Life ◽  
Disease Severity ◽  
Ammonium Compound ◽  
Conidial Suspension ◽  
Rooted Cuttings ◽  
Disease Progress ◽  
Progress Curve ◽  
Significant Difference ◽  
Plant Transfer ◽  
Blight Disease

Calonectria pseudonaviculata and Pseudonectria foliicola causing the infamous ‘boxwood blight’ and ‘Volutella blight’, respectively, are a constant threat to the boxwood production and cut boxwood greenery market. Both pathogens cause significant economic loss to all parties (growers, retailer, and customers) in the horticultural chain. The objective of this study was to evaluate efficacy of disinfesting chemicals [quaternary ammonium compound (QAC), peroxy, acid, alcohol, chlorine, cleaner] in preventing plant-to-plant transfer of C. pseudonaviculata and P. foliicola via cutting tools, as well as reduction of postharvest boxwood blight and Volutella blight disease severity in harvested boxwood greenery. First, an in vitro study was conducted to select products and doses that completely or near-completely inhibited conidial germination of C. pseudonaviculata and P. foliicola. The selected treatments were also tested for their ability to reduce plant-to-plant transfer of C. pseudonaviculata and P. foliicola and manage postharvest boxwood blight and Volutella blight in boxwood cuttings. For the plant-to-plant transfer study, Felco 19 shears were used as a tool for mechanical transfer of fungal conidia. The blades of Felco 19 shears were exposed to a conidial suspension of C. pseudonaviculata or P. foliicola by cutting a 1 cm diameter cotton roll that had been dipped into a fungal suspension. Disease-free boxwood rooted cuttings (10 cm height) were pruned with the contaminated shears. The Felco 19 shears were equipped with a mounted miniature sprayer connected to a pressurized reservoir of treatment solution that automatically sprayed the blade and plant surface while cutting. The influence of accumulated sap on the shear blade was studied through 1- or 10-cut pruning variable on test plants and screened for the efficacy of treatments. Then, the boxwood rooted cuttings were transplanted and incubated in room conditions (21 °C, 60% RH) with 12 h of fluorescent light; data evaluation on disease severity was done weekly for a month. Disease progress [area under disease progress curve (AUDPC)] was calculated. In another study, postharvest dip application treatments were used for the management of postharvest boxwood blight or Volutella blight on boxwood cuttings. The harvested boxwood cuttings were inoculated with a conidial suspension of C. pseudonaviculata or P. foliicola, then dipped into treatment solution 3 days afterwards. The treated boxwood cuttings were kept in room conditions, and boxwood blight or Volutella blight disease severity as well as marketability (postharvest shelf life) assessed every 2 days for 1 week. A significant difference between treatments was observed for reduction of boxwood blight or Volutella blight severity and AUDPC. The treatments (ODD + DoD + DdD + DB)AC [Simple Green D Pro 5], 2 propanol + DDAC (0.12%) [KleenGrow], and DBAC + DEAC [GreenShield] were the most effective in reducing the plant to plant transfer of boxwood blight and Volutella blight when pruned with contaminated Felco 19 shears. In addition to the three effective treatments above, acetic acid (2.5%) [Vinegar], 2-propanol + DDAC (0.06%), sodium hypochlorite (Clorox) and potassium peroxymonosulfate + NaCl (2%) [Virkon] were effective in reducing postharvest boxwood blight whereas DBAC + DBAC [Lysol all-purpose cleaner], ethanol [70% (Ethyl alcohol)] and DDAC +DBAC [Simple Green D Pro 3 plus] were effective in reducing Volutella blight disease severity and AUDPC, and also maintained better quality and longer postharvest shelf life of boxwood cuttings when applied as a dip treatment. The longer postharvest shelf life of boxwood cuttings noted may be attributed to reduced disease severity and AUDPC resulting in healthy boxwood cuttings.

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

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