Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Ngugi, Lawrence C.; Abelwahab, Moataz; Abo‐Zahhad, Mohammed

doi:10.1016/j.inpa.2020.04.004

Cited by 268 publications

(162 citation statements)

References 90 publications

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“…The Histogram of Gradients (HOG) model can be used for fast feature appearance extraction [ 62 ]. A spatial grey-level dependence matrix can indicate THE orientation, distance, location and size of an ROI [ 24 , 63 ]. Auto-Regression (AR) has a degree of randomness that enables prediction of the colour cell based on the data of the previous cell.…”

Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

“…Then, features were then fed to an M-class SVM, which achieved 92.75% as accuracy. Other methods, e.g., genetic algorithms (GA) and Particle Swarm Optimisation (PSO), have also been proposed for lesion segmentation [ 24 ].…”

Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

“…However, the high achieved accuracy of k-mean clustering algorithms [ 21 ] is still in need of more computational time concerning the index validity term. Another key problem is the segmentation sensitivity towards the region of interest (ROI) determination [ 22 , 23 , 24 ]. Researchers have attempted to hybridise unsupervised models with specific statistical measures [ 25 , 26 ] or artificial intelligence algorithms to enhance their performance.…”

Section: Introductionmentioning

confidence: 99%

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

144

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

144

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“…On the shape recognition side, solutions are as numerous as their application fields. Shape recognition is nowadays widely investigated in machine learning approaches [ 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial Location in Integrated Circuits through Infrared Microscopy

Abele

Damoiseaux

Moubtahij

et al. 2021

Sensors

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In this paper, we present an infrared microscopy based approach for structures’ location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented—discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.

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“…In (11) have reviewed automated disease recognition and Cite (6) and commented that it would be better to provide the performance of the system on a larger dataset. The same comment can be applied to (7)(8)(9)(10) .…”

Section: Introductionmentioning

confidence: 99%

An effective approach to feature extraction for classification of plant diseases using machine learning

Jeyalakshmi¹,

Radha²

2020

IJST

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Objectives: To make automatic classification of diseased potato and grape leaf from normal potato and grape leaf. Methods: Experimental sample size of 3000 and 4270 Potato and Grape leaf images were used respectively. The diseased and healthy leaf image samples were taken from PlantVillage dataset. The color features viz., average Red, Green, Blue and Hue intensities of Lesion region were calculated. Features namely Contrast, Dissimilarity, Homogeneity, Energy, Correlation, ASM, and Entropy were extracted from hue lesion region. Also, histogram features such as mean and standard deviation were extracted from hue infected region. Then, data normalization was done on feature set to bring all features into a common scale. Finally, Naïve Bayes, K Nearest Neighbor and Support Vector Machine Classifiers were applied on the above said feature sets. Findings: The Dataset was split in the ratio of 80% and 20% for training and test sets. The classifiers NB, KNN and SVM classified Potato leaves with an accuracy of 88.67%, 94.00% and 96.83% respectively and Grape leaves with an accuracy of 81.87%, 93.10% and 96.02% respectively. For both the species, SVM classifier gave the highest accuracy. Also, it was found that the proposed method performs well as compared with the related works in the literature. Novelty/Applications: An effective feature extraction method to classify grape and potato diseases was proposed in this research work. Also, it was found that the proposed method performs well as compared with the related works in the literature.

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Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Cited by 268 publications

References 90 publications

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Spatial Location in Integrated Circuits through Infrared Microscopy

An effective approach to feature extraction for classification of plant diseases using machine learning

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