Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images

Mwebaze, Ernest; Owomugisha, Godliver

doi:10.1109/icmla.2016.0034

Cited by 72 publications

(35 citation statements)

References 9 publications

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“…Here, viral disease attacks on crops is a leading cause of food insecurity and poverty. Traditional disease surveillance methods fail to provide adequate information to curtail the impact of diseases Mwebaze and Biehl [2016], Mwebaze and Owomugisha [2016], Quinn et al [2011]. The Cassava Adhoc Surveillance Project from Makerere University implements crowd-sourcing surveillance using pictures taken by mobile phones in order to address this gap Mutembesa et al [2018].…”

Section: Access To Opportunity In the Developing Worldmentioning

confidence: 99%

Mechanism design for social good

Abebe

Goldner

2018

AI Matters

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Across various domains---such as health, education, and housing---improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an outsized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of inter-disciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities.

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Section: Access To Opportunity In the Developing Worldmentioning

confidence: 99%

Mechanism design for social good

Abebe

Goldner

2018

AI Matters

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“…The method that was proposed by Owomugisha and Mwebaze utilized a Linear SVC classifier to classify the plant leaf diseases according to levels [14]. The levels were classified as Healthy class, Level 2 severity, level 3 severity, and level 4 severity.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Sumbwanyambe

2019

AgriEngineering

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This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.

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“…In such circumstances, methodologies for automated plant diagnosis characterized by accuracy, speed and low costs have been requested by the agricultural industry. Several studies have been carried out in response to such requests [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [4] used support vector machines (SVM) to classify rice plant diseases and attained 92.7% accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In [7] also used an artificial neural network classifier and showed 87.8% in fungal disease diagnosis. In [12] discriminated cassava diseases in five categories (four diseases and a healthy state) and estimated their severity in five grades from healthy (= 1) to terminal (= 5). They used a combination of their original feature descriptors and classifiers such as linear SVM.…”

Section: Introductionmentioning

confidence: 99%

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

Fujita¹,

Uga²,

Kagiwada³

et al. 2018

IJET

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An accurate, fast and low-cost automated plant diagnosis system has been called for. While several studies utilizing machine learning techniques have been conducted, significant issues remain in most cases where the dataset is not composed of field images and often includes a substantial number of inappropriate labels. In this paper, we propose a practical automated plant diagnosis system. We first build a highly reliable dataset by cultivating plants in a strictly controlled setting. We then develop a robust classifier capable of analyzing a wide variety of field images. We use a total of 9,000 original cucumber field leaf images to identify seven typical viral diseases, Downy mildew and healthy plants including initial symptoms. We also visualize the key regions of diagnostic evidence. Our system attains 93.6% average accuracy, and we confirm that our system captures important features for the diagnosis of Downy mildew.

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Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images

Cited by 72 publications

References 9 publications

Mechanism design for social good

Mechanism design for social good

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

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

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