scholarly journals Assessing sugarcane brown rust resistance using Image analysis

Bionatura ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 1698-1703
Author(s):  
Yaquelin Puchades-Izaguirre Puchades-Izaguirre ◽  
Mónica Tamayo-Isaac ◽  
Wilfre Abiche-Maceo ◽  
Reynaldo Rodríguez-Gross ◽  
María La O Hechavarría ◽  
...  
Keyword(s):  
Image Analysis ◽  
Disease Resistance ◽  
Rust Resistance ◽  
Digital Image Analysis ◽  
Brown Rust ◽  
Disease Assessment ◽  
Visual Estimation ◽  
Precise Method ◽  
Sugarcane Brown Rust

Image analysis provides an accurate and precise method of pest evaluation. This work's objective was to compare the usefulness of the ImageJ® 1.43u image processor and visual estimation as methods to characterize brown rust lesions and estimate the resistance of new sugarcane cultivars. For this, leaves images of 10 cultivars were captured, and the parameters quantity, most regular size of the pustules, and leaf area affected were determined. The data were correlated with the eight control (standard) genotypes' evaluations to obtain a classification of disease resistance. The results showed that the software's determinations were the most accurate, although all the methods were reliable for rating the reaction to brown rust. Therefore, it is proposed to move away from visual disease assessment toward a system based on digital image analysis.

Classification of Oryza Sativa using Digital Image Analysis

2019 ◽  
Author(s):  
Davood Pour Yousefian Barfeh ◽  
Patrice Xandria Mari Delos Reyes ◽  
Myrna Coliat ◽  
Favis Joseph Balinado ◽  
Jessie Montalbo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Oryza Sativa ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals A nondestructive method to assess rot root severity of Lactuca sativa L

2013 ◽  
Vol 66 ◽  
pp. 375-375
Author(s):  
P. Suvarnaphaet ◽  
V. Tanmala ◽  
M. Kanjanamaneesathian
Keyword(s):  
Image Analysis ◽  
Disease Severity ◽  
Digital Image ◽  
Lactuca Sativa ◽  
Root Rot ◽  
Digital Image Analysis ◽  
Disease Assessment ◽  
Root Rot Disease ◽  
Diseased Root ◽  
Rot Disease

Root rot disease caused by aquatic fungi such as Aphanomyces sp and Pythium sp is prevalent in Lactuca sativa grown in a dynamic root floating technique (DRFT) hydroponic system in Phetchaburi College of Agriculture and Technology Thailand Roots of this plant have been severely infected with these fungi and plant growth has been affected resulting in a decline of yield over time Symptoms of root rot are initially characterised by tissue discoloration in some parts of the root followed by a loss of tissue integrity of the whole root In the DRFT system root rot symptoms of L sativa range from severe (complete) root rot to a healthylooking root (no symptoms)This study investigated the potential for using digital images as an objective tool for assessing disease severity with the aim of the tool being applied by assessors with no previous experience in disease assessment Lactuca sativa that had been grown in DRFT for 30 days and had various degrees of root rot symptoms were selected for the study Visual evaluation of the diseased root indicated that there were six levels of disease severity with root colour ranging from white (healthy looking) to completely black (severe root rot)When the diseased root samples were subjected to digital image analysis between the black and white extremes there was one shade of grey and three shades of brown The images of these six levels of severity were analysed using Hunter L a and b values It was found that the six levels of root rot severity could be distinguished based upon the L parameters The value of Lightness (Hunter L) which is transformed from RGB digital image (using a color calculator in http//wwweasyrgbcom) decreased exponentially (from severity level 1 to 6) with a constant value at 048 This indicates that digital image analysis using this simple tool can be utilised to objectively assess root rot disease in L sativa Once this technology is thoroughly studied developed and validated for lettuce growing in the DRFT system there is potential for it to be a useful tool to assist lettuce growers in making a decision to implement control measures

Developing genetically segregating populations for localization of novel sugarcane brown rust resistance genes

Euphytica ◽  
2019 ◽  
Vol 215 (10) ◽  
Author(s):  
Xiao-Yan Wang ◽  
Wen-Feng Li ◽  
Ying-Kun Huang ◽  
Hong-Li Shan ◽  
Rong-Yue Zhang ◽  
...  
Keyword(s):  
Resistance Genes ◽  
Rust Resistance ◽  
Brown Rust ◽  
Rust Resistance Genes ◽  
Sugarcane Brown Rust

Ground cover in temperate native perennial grass pastures. I. A comparison of four estimation methods

2002 ◽  
Vol 24 (2) ◽  
pp. 288 ◽  
Author(s):  
S. R. Murphy ◽  
G. M. Lodge
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Perennial Grass ◽  
Canopy Cover ◽  
Ground Cover ◽  
Estimation Methods ◽  
Visual Estimation ◽  
Pasture Production ◽  
Point Quadrat

Studies were conducted to compare visual estimates of ground cover and canopy cover by both inexperienced and experienced observers and to compare those estimates with those from more objective methods in native pastures in the high rainfall, temperate rangelands of northern NSW. Ground cover and canopy cover of 60 quadrats was estimated using visual, mapped area, digital image analysis and photo point quadrat methods. Inexperienced observers were trained by estimating ground cover of reference quadrats. Differences between mean visual estimates of ground cover and canopy cover for experienced and inexperienced observers were not significant (P>0.05). Mean ground cover estimates by the mapped area, digital image analysis and point quadrat methods were also not different from each other. The overall relationship between mean visual estimate and mean objective estimate of ground cover was non-linear (second order polynomial, R2 = 0.93), observers tending to underestimate in the mid-range (20 to 80%) of cover compared with objective methods. Mean visual estimate of ground cover was 73.7% compared with the mean objective estimate of 83.7%. Visual estimates of canopy cover (mean 34.6%) were highly correlated (R2 = 0.90) with those of the mapped area method (mean 34.3%) and the relationship was linear. Measurement of ground cover is a standard technique used in many pasture ecology and management studies and is increasingly being used by land managers to monitor pasture production and sustainability. Inexperienced observers were trained quickly and easily to estimate ground cover and canopy cover with sufficient accuracy to identify ranges of cover using visual estimation, indicating that the visual estimation technique should be suitable for estimating ground cover in land management research.

scholarly journals ANALISIS GAMBAR DIGITAL UNTUK SERANGAN PENYAKIT LAYU FUSARIUM DI PISANG MENGGUNAKAN IMAGEJ

2020 ◽  
Vol 19 (3A) ◽  
Author(s):  
Ahmad Zaelani ◽  
Wulan S. Kurniajati ◽  
Herlina Herlina ◽  
Diyah Martanti ◽  
Fajarudin Ahmad
Keyword(s):  
Image Analysis ◽  
Fusarium Oxysporum ◽  
Digital Image ◽  
Plant Material ◽  
Digital Image Analysis ◽  
Wilt Disease ◽  
Disease Assessment ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Percentage Area

Fusarium wilt disease caused by Fusarium oxysporum f. sp. cubense is the most dangerous disease in banana. Recently, development of new banana varieties has been the most effective ways to prevent this disease. To develop the resistant banana, Fusarium severity analysis is the important part in the process of Fusarium disease assessment to quantify the disease severity.. The objective of this study was to develop digital image analysis method for Fusarium severity analysis by using software ImageJ. Pisang Ambon and Pisang Cavendish were used as plant material due to its susceptibility of the disease. Fusarium severity analysis performed as follows (i) Photographing of Fusarium-infected rhizom (ii) Digital image analysis by using ImageJ of the taken image. The analysis result was percentage area of Fusarium-infected rhizom, represented by necrosis and discoloration. The percentage of rhizome infected by Fusarium- of Pisang Ambon#1 was 50.10%, while Pisang Ambon#2 was 22.23%. In addition, the percentage of Pisang Cavendish#1 and Pisang Cavendish#2 was 28.52% and 39.5%, respectively. Digital image analysis of the sample showed consistent result and more objective. Development of the digital image analysis is not only useful for Fusarium severity analysis in Banana, but also for other crops.   

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