Feature Extraction for Diseased Leaf Image Classification using Machine Learning

Nandhini, N.; Bhavani, Rao R.

doi:10.1109/iccci48352.2020.9104203

Cited by 25 publications

(10 citation statements)

References 6 publications

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“…This model has 98.0% accuracy. For identifying diseases in plant leaves, Nandhini and Bhavani [ 25 ] offered machine learning methods such as KNN, decision trees, and SVM. To segment the diseased part of the leaf image, they employed a feature extraction technique that involves several steps, including converting RGB images to lab color space models for color feature extraction, K-means clustering, fast Fourier transform, and histogram, scale-invariant feature transform for shape feature extraction, and principal component analysis for lowering vector size.…”

Section: Related Workmentioning

confidence: 99%

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Elaraby

Hamdy

Alanazi

2022

Computational Intelligence and Neuroscience

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Most plant diseases have apparent signs, and today’s recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist’s talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system’s total performance reached 94% at its best. The proposed approach outperforms the existing methods.

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Section: Related Workmentioning

confidence: 99%

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Elaraby

Hamdy

Alanazi

2022

Computational Intelligence and Neuroscience

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“…Similarly, we can use AI-Tools to detect early diseases and help farmers improve agricultural growth to obtain location-based information for precision agriculture [32]. In [33], the author used the SVM classifier to classify lesion images from healthy leaf images with a maximum accuracy of 91%. Their process involves extracting different features (such as shapes and colors) and then using machine learning models for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf Blights Detection and Classification in Large Scale Applications

Fayyaz¹,

Al‐Dhlan²,

Rehman³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to analyze the different leaves of plants by themselves when recognizing diseases. In this article, a variety of machine learning methods are used to classify and detect leaf blight. We use the fusion of deep convolutional neural network (CNN) models obtained from SqueezeNet and ShuffleNet to improve the accuracy and robustness of large-scale applications. We use entropy to reduce the complexity of the calculation process and reduce the features in the deep learning process. In addition, we use a support vector machine (SVM) classifier to obtain the classification. We use the CIELAB color space to capture the entire color range to improve accuracy. Our results are very promising because we have achieved 98% accuracy in the early detection of leaf blight.

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“…Recently, several classifiers have been applied to detect plant diseases [25][26][27][28], presenting different accuracies. Multiclass deep learning techniques have also been applied to discriminate citrus plant diseases (anthracnose, black spot, canker, scab, HLB, and melanose) with 94% accuracy [29].…”

Section: Introductionmentioning

confidence: 99%

Multiclass classifier based on deep learning for detection of citrus disease using fluorescence imaging spectroscopy

Neves¹,

Wetterich²,

Sousa³

et al. 2023

Laser Phys.

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In this work, we have combined fluorescence imaging spectroscopy (FIS) and supervised learning methods to identify and discriminate between citrus canker, Huanglongbing, and other leaf symptoms. Our goal is to differentiate these diseases and nutrient conditions without prior eye assessment of symptoms. Five supervised learning methods were evaluated. Our results show that by combining FIS with a convolutional neural network (AlexNet), it is possible to identify the disease of a sample with up to 95% accuracy. An enormous gain of time and a substantial cost reduction were achieved by this approach compared to polymerase chain reaction-based methods.

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Feature Extraction for Diseased Leaf Image Classification using Machine Learning

Cited by 25 publications

References 6 publications

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Classification of Citrus Diseases Using Optimization Deep Learning Approach

Leaf Blights Detection and Classification in Large Scale Applications

Multiclass classifier based on deep learning for detection of citrus disease using fluorescence imaging spectroscopy

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