Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Sibiya, Malusi; Sumbwanyambe, Mbuyu

doi:10.3390/pathogens10020131

Cited by 23 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common rust causes patches to appear on the leaves of maize plants that resemble rust on metal objects. According to some reports, typical rust infections can reduce the yield of maize grains by up to 40% on average [26] [27]. The Cercospora zeae-maydis fungus is what causes Grey leaf spot.…”

Section: Maize Leaf Diseasesmentioning

confidence: 99%

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Handa

Krishan

2023

Preprint

View full text Add to dashboard Cite

In this world of digitization, most of the data is in the form of images acquired using camera. Image enhancement plays a vital role in the quality improvement of digital images. In this work, a combined approach based on the contrast limited adaptive histogram equalization (CLAHE) and Retinex algorithm is proposed. It is a wavelet based Retinex algorithm with adaptive histogram equalization and gaussian filter. First, image is enhanced using CLAHE, image is decomposed using Daubechies wavelet and then followed by the Retinex algorithm, which used low frequency components to enhance the image. Lastly, a gaussian filter is used to smoothen the image. The dataset of maize leaf disease is used for the analysis of quality enhancement and denoising. It is clear from the results that the proposed method improves the quality by reducing the noise of the maize leaf images. Theses refined images can be used for maize leaves disease detection and classification system to achieve high accuracy.

show abstract

Section: Maize Leaf Diseasesmentioning

confidence: 99%

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Handa

Krishan

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Sibiya and Sumbwanyambe [58] AI has enabled many plant pathology applications. For plant disease categorization, numerous researchers used pre-trained CNNs including VGG-16, Inception, and Google Net.…”

Section: Related Workmentioning

confidence: 99%

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Hasan

Khidhir

2023

IJECE

View full text Add to dashboard Cite

<p class="IEEEAbtract"><span lang="EN-US">Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed.</span></p>

show abstract

“…The red and green components aid in identifying the yellow portions of the image, typically indicated as infected. Fuzzy logic is an effective method for solving disease classification issues ( Sibiya and Sumbwanyambe, 2021 ), the author proposes the minimum distance approach, a genetic algorithm modification, to locate a plant’s infected portion for picture segmentation ( Ngugi et al., 2021 ). After picture segmentation, the author examined the accuracy of the technique using different classification algorithms, such as k mean clustering and SVM ( Bargelloni et al., 2021 ).…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Shoaib

Shah²,

Hussain³

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

show abstract

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Cited by 23 publications

References 32 publications

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Toward enhancement of deep learning techniques using fuzzy logic: a survey

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Contact Info

Product

Resources

About