An automated detection and classification of citrus plant diseases using image processing techniques: A review

Iqbal, Zahid; Khan, Muhammad Attique; Sharif, Muhammad; Shah, Jamal Hussain; Rehman, Muhammad Habib ur; Javed, Kashif

doi:10.1016/j.compag.2018.07.032

Cited by 345 publications

(167 citation statements)

References 63 publications

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“…A genetic algorithm was developed for feature optimization and achieved results with 98% accuracy from the experimental work performed on two publicly available datasets, plant-village and CASC-IFW. [29] A noteworthy article [30] reviewed a number of plant diseases in general and also covers the classification of citrus plant diseases in specific. It also covered the limitations and research dimensions of different machine learning techniques in citrus plant diseases.…”

Section: Introductionmentioning

confidence: 99%

Machine vision approach for classification of citrus leaves using fused features

Qadri

Husnain

et al. 2019

International Journal of Food Properties

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The objective of this study was to observe the potential of machine vision (MV) approach for the classification of eight citrus varieties. The leaf images of eight citrus varieties that were grapefruit, Moussami, Malta, Lemon, Kinow, Local lemon, Fuetrells, and Malta Shakri. These were acquired by a digital camera in an open environment without any complex laboratory setup. The acquired digital images dataset was transformed into the multifeature dataset that was the combination of binary, histogram, texture, spectral, rotational, scalability and translational (RST) invariant features. For each citrus leaf image, total 57 multi-features were acquired on every non-overlapping region of interest (ROI), i.e. (32x32), (64x64), (128x128), and (256x256). Furthermore, the optimized 15 features using the supervised correlation-based feature selection (CFS) technique were acquired. The optimized multi-features dataset to different MV classifiers namely Multilayer Perceptron (MLP), Random Forest (RF), J48 and Naïve Bayes using10-fold cross-validation method were plugged-in. The results produced by MLP presented an average overall accuracy of 98.14% on ROIs (256x256) outperforming the other classifiers. The classification accuracy values by MLP on the eight citrus leaf varieties,

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Section: Introductionmentioning

confidence: 99%

Machine vision approach for classification of citrus leaves using fused features

Qadri

Husnain

et al. 2019

International Journal of Food Properties

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“…They described presently the technologies that include spectroscopic and image capturing based plant disease detection methods for monitoring strength and disease identification in plants under field conditions [7]. In [8][9][10], image processing disease recognition approach was used for plant disease diagnostics. Chaudhary et al extracted color features, disease spots were identified by extracting some attributes such as shape feature method.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Classification using Deep Learning Google Net Model

2019

IJITEE

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Plant diseases have been a major crisis that is disturbing the food production. So there is a need to provide proper procedures for plant disease detection at its growing age and also during harvesting stage. Timely disease detection can help the user to respond instantly and sketch for some defensive actions. This detection can be carried out without human intervention by using plant leaf images. Deep learning is progressively best for image detection and classification. In this effort, a deep learning based GoogleNet architecture is used for plant diseases detection. The model is trained using public database of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82% accuracy for 14 crop types making it capable of further deployment in a crop detection and protection application.

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“…Different image pre-processing techniques, segmentation, feature extraction, feature selection and classification methods were tried out on citrus plant leaves. This study briefs its strengths and limitations and discusses further issues (Iqbal et al, 2018). Another study proposed a methodology that involved an ontology-based approach to plant diseases and discussed the approach by analyzing rice disease pattern.…”

Section: Introductionmentioning

confidence: 99%

Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

Rishiikeshwer

Shriram

Raju

et al. 2020

Journal of Computer Science

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In farming, crops are prone to a wide variety of diseases. The impact of sudden climatic change has adverse effects on their growth, providing incubation to harmful viruses and bacteria. Diseases to crops imply a significant negative impact on health, economy and livelihood of the human population. According to the data from the Food and Agricultural Organization (FAO), an average of 1.3 billion tonnes of food crops succumb to such diseases annually. This paper presents an approach to prevent such diseases from propagating, by early diagnosis of such abnormalities in leaves using state of the art deep learning techniques. Convolution Neural Networks based model with additional pre-processing techniques are developed to classify the leaves into affected and healthy category. Various Deep Learning architectures and hyperparameter tuning were carried out and the resulting model produces an accuracy of up to 95% with 400 actual leaf images and up to 98% with 3600 augmented datasets. The models are trained on reallife leaf images of crops, captured from an actual agricultural field. A user-intuitive IoT Web Application is developed to capture, process and display the predicted result (disease status) from the model.

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An automated detection and classification of citrus plant diseases using image processing techniques: A review

Cited by 345 publications

References 63 publications

Machine vision approach for classification of citrus leaves using fused features

Machine vision approach for classification of citrus leaves using fused features

Plant Disease Classification using Deep Learning Google Net Model

Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

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