Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Hlaing, Chit Su; Zaw, Sai Maung Maung

doi:10.1109/icis.2018.8466483

Cited by 75 publications

(36 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The images of the plants have three key features, namely, color, shape, and texture. Compared to color and texture, the shape feature cannot help find the plant's diseases [48]. Hlaing and Zaw [48] classified tomato plant disease using a combination of texture and color features.…”

Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

“…Compared to color and texture, the shape feature cannot help find the plant's diseases [48]. Hlaing and Zaw [48] classified tomato plant disease using a combination of texture and color features. They used the Scale Invariant Feature Transform (SIFT) to find the texture information, containing details about the shape, location, and scale.…”

Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan

Albattah

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.

show abstract

Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

Section: Feature Extraction For Disease Identificationmentioning

confidence: 99%

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan

Albattah

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Image is processed using traditional Image processing methods and SIFT algorithm is passed through the preprocessed image to extract the color feature, where SIFT texture feature is described using Johnson SB distribution. Average precision is calculated using cross validation of 10fold [6]. Analysis of key point extraction is done using SIFT and SURF extraction methods, it is proved that SIFT process is more accurate than SURF.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Gangadharan¹,

Rosline²,

Dhanasekaran³

et al. 2020

IJACSA

View full text Add to dashboard Cite

The diagnosis of plant disease by computer vision using digital image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Timely addressal of these ailments can be the difference between the prevention and perishing of an ecosystem. To make the system more efficient and feasible we have proposed an algorithm called Enhanced Fusion Fractal Texture Analysis (EFFTA). The proposed method consists of Feature Fusion technique which combines SIFT-Scale Invariant Feature Transform and DWT-Discrete Wavelet Transform based SFTA-Segment Based Fractal Texture Analysis. Image as a whole can be detected by shape, texture and color. SIFT is used to detect the texture feature, it extracts the set of descriptors that is very useful in local texture recognition and it captures accurate key points for detecting the diseased area. Further extraction of texture is considered and that can be performed by WSFTA method. It adopts intra-class analysis and inter-class analysis. Extracted features trained using Back Propagation Neural Network. It improves and expands the success rate and accuracy of extraction also it provides higher precision and efficiency when compared to the other traditional methods.

show abstract

“…In supervised training, the preparation information comprises an arrangement of preparing cases, where every illustration is a couple comprising of information and expected yield esteem. [20] A regulated learning algorithm investigates the preparation information and after that predicts the right classification forgiven informational index input. For example, the teacher teaches the student to identify orange and lemon by giving some features of that.…”

Section: H Support Vector Machinementioning

confidence: 99%

Highly Accurate Melanoma Detection from Skin Images using Multiple Feature Extraction and Support Vector Machine (SVM)

A.S*,

Graceline.H,

Jey J B

2020

IJRTE

View full text Add to dashboard Cite

Digital image processing (DIP) plays a major role in the biomedical image segmentation and classification process. Melanoma detection from skin images is most widely using the application in biomedical imaging. In this paper, a novel algorithm for Melanoma Detection from digital images using multiple feature extraction with a Support Vector Machine (SVM) for highly accurate classification. Multiple features such as color, texture and statistical features are considered to train the SVM. To increase the classification sensitivity, feature extraction is done in (Red, Green, Blue) RGB and (Hue, Intensity, Saturation) HIS color domains. Two separate modules for training and testing is performed with collected sample data by using medical experts. To separate the region from the skin, a cantor-based segmentation technique is used in the gray level component of the input image. The proposed method is tested with various images that are collected from different patients from different places. Form the result validation it is clear that the proposed algorithm can give maximum accuracy of 95% which is best when compared to the conventional classification algorithms

show abstract

Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Cited by 75 publications

References 10 publications

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Highly Accurate Melanoma Detection from Skin Images using Multiple Feature Extraction and Support Vector Machine (SVM)

Contact Info

Product

Resources

About