An Algorithm for Severity Estimation of Plant Leaf Diseases by the Use of Colour Threshold Image Segmentation and Fuzzy Logic Inference: A Proposed Algorithm to Update a “Leaf Doctor” Application

Sibiya, Malusi; Sumbwanyambe, Mbuyu

doi:10.3390/agriengineering1020015

Cited by 32 publications

(21 citation statements)

References 14 publications

(22 reference statements)

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“…Mobile device-based applications generally require the user to set thresholds, which can lead to inconsistencies (Bock et al 2008a(Bock et al , 2009c. Software was recently developed automating severity estimation using Fuzzy Logic rules and image segmentation for the mobile application 'Leaf Doctor' (Sibiya and Sumbwanyambe 2019).…”

Section: Application In Research and Practicementioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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Section: Application In Research and Practicementioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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“…These three fusions of segmentation algorithms with different colour spaces were used in the segmentation stage in the study done by Aravind et al [32], Mohanapriya et al [33] and Sibiya et al [34] to extract the symptomatic patterns on the leaf of maize from the same dataset which is PlantVillage image dataset. The outcome of their work has been reviewed, analysed and finally being compared with the proposed method in this study.…”

Section: Accuracy Of Algorithmsmentioning

confidence: 99%

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni¹,

Zulcaffle²,

Kipli³

2020

j.asep

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Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.

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“…Visual estimate of DS was obtained by observing the disease area covered in the spike and assigned a corresponding severity value from 0 to 100%. In this study, image analysis disease measurements were achieved by manually measuring spike disease area (pixels) using RGB color threshold segmentation with the image analysis software Fiji ImageJ v.1.52a (Schindelin et al, 2012;Sibiya and Sumbwanyambe, 2019). First, the measurement of the total spike area was obtained, then the diseased area was measured.…”

Section: Data Collection Ds and Disease Measurementsmentioning

confidence: 99%

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks

et al. 2021

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Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

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An Algorithm for Severity Estimation of Plant Leaf Diseases by the Use of Colour Threshold Image Segmentation and Fuzzy Logic Inference: A Proposed Algorithm to Update a “Leaf Doctor” Application

Cited by 32 publications

References 14 publications

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks

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