Automated extraction of digital images features of three kinds of cotton leaf diseases

Rothe, Prashant R.; Kshirsagar, R. V.

doi:10.1109/icecce.2014.7086637

Cited by 29 publications

(8 citation statements)

References 11 publications

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“…Feature Extraction. Rothe and Kshirsagar [27] discussed the identification of diseases utilizing picture preparing strategies that extracted the highlights, for example, region, major and minor axes, direction, and so on, from pictures of influenced ailment leaves. e paper has two strategies, in particular, image enhancement and image segmentation.…”

Section: Disease Detection Analysis With Image Segmentation Andmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Kumar¹,

Jain

Shukla

et al. 2021

Mathematical Problems in Engineering

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Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

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Section: Disease Detection Analysis With Image Segmentation Andmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Kumar¹,

Jain

Shukla

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Eq. (6) shows that the wavelet function ( ) and the scaling function ( )can be used (7). The wavelet atoms are described by three mother atoms and scaling , and .…”

Section: Feature Extractionmentioning

confidence: 99%

“…If any of these errors occur, the result could be incorrect. Foliar illnesses can be identified by a machine, which can help the farmer monitor plant growth and boost productivity [6][7][8]. As it shows on plant leaves, it also aids in early disease detection [8].…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

2021

IJATCSE

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Plants are seen as vital because they provide mankind with energy. Plant diseases can harm the leaf at any time between planting and harvesting, resulting in enormous losses in crop output and market value. A leaf disease detection system acts asignificant role in agricultural production. A large amount of labour is required for this process as well as an in-depth understanding of plant diseases. Determining the presence of illnesses in plant leaves requires the use of deep learning and machine learning methods, which classify the data based on a specified set. In this paper, apple and tomato leaves disease detection process is carried out by Chaotic Salp Swarm algorithm (CSSA) followed by Bi-directional Long Short Term Memory (Bi-LSTM) technique for classification. We've used the Bi-LSTM architecture to sense disease in tomato and apple leaves in studies. In order to determine the type of leaves, we trained a deep learning network using the PlantVillage dataset of damaged and healthy plant leaves. It is estimated that the trained model achieves a test accuracy of 96%.

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“…Their effect is observed as blurring of the image. The output of Gaussian filter is a 'weighted average' of each pixel's neighborhood and the average is weighted more towards the value of central pixel [18]. Siddharth Singh Chouhan1, Ajay Kaul1, et al says that Region growing algorithm is used for image segmentation technique.…”

Section: Fig 2 Rust Sporesmentioning

confidence: 99%

“…The image is stored as matrix of discrete values a discrete approximation of Gaussian function is to be performed before convolution. The output of Gaussian filter is a 'weighted average' of each pixel's neighborhood and the average is weighted more towards the value of central pixel [18].…”

Section: )mentioning

confidence: 99%

Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

Kavipriya¹

2020

IJRASET

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Agriculture plays a vital role in livelihood. Agriculture is the only way to feed ever growing populations. Several researchers are going on energetically in plant disease detection. Plant infection can affect the leaf which leads to huge loss in cultivation of crop and economical value of market. Mainly, in between the stages of sowing and harvesting. Plant infection are caused by various pests like virus, fungus and bacteria which causes infection to plants and results in loss of quantity and amount of production. Also, plant diseases increase due to pollution and other environmental changes. The plants should be supervised from its germinal stage of their life-cycle to evade such diseases. To monitor such type of infection manually is extremely difficult and also time consuming. It is necessary to devise a method to speed up this identification process. This technique is imperative, so it is decided to automate the disease detection system. Hence, image processing is used for the recognition of plant infection. Here, matching and segmentation algorithms are used for identifying the diseases. Classification algorithm is used for classifies the disease. Machine learning techniques are used for improving the diagnosis of plant infection. Identification of pesticides for those affected diseases is also suggested.

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Automated extraction of digital images features of three kinds of cotton leaf diseases

Cited by 29 publications

References 11 publications

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

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