Monitoring the Turmeric Finger Disease and Growth Characteristics Using Sensor Based Embedded System —A Novel Method

Shanmugam, M.; Ramasamy, Asokan; Paramasivam, Sivaranjani; Prabhakaran, Priyadharshini

doi:10.4236/cs.2016.78112

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…[5] Image segmentation, according to the 2021 publication "On Comparison of Different Image Segmentation Techniques," is the division of image pixels into discrete zones that are either identical or as closely associated as feasible in terms of brightness, texture, or color. Numerous images processing tasks, including object detection, machine vision, image compression, and image treatment, can benefit from picture segmentation [10]. This work will offer a thorough analysis of several image segmentation methods.…”

Section: Survey Of Work Done In the Research Area And Need For More R...mentioning

confidence: 99%

Turmeric Plant Leaf Oversight, Diagnosis and Recommendation for Control of Diseases by Raspberry Pi 4 and Pi Camera System

Mahadeo Pisal,

Snehal Jadhav,

Urmila Patil

2024

Int Res J Adv Engg Hub

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In the agriculture industry, disease identification is vital. As a rhizomatous crop, turmeric is widely recognized for its therapeutic use. It is vital to keep an eye on these crops. The most common diseases that affect turmeric leaves are leaf spots, leaf blotch, brown rot, dry rot, and leaf blight. The research creates a hardware system that enables early disease diagnosis and prevention before it affects the entire crop, resulting in high-quality agricultural production. Using k-means image segmentation, a database of various leaf pictures will be generated and handled. Pi camera interfaced with Raspberry Pi 4 hardware will be used to capture images of infected plants and will be used for further processing and analysis. Pesticides or insecticides will be recommended to farmers according to the status of plant disease through the use of friendly communication like WhatsApp or SMS.

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Section: Survey Of Work Done In the Research Area And Need For More R...mentioning

confidence: 99%

Turmeric Plant Leaf Oversight, Diagnosis and Recommendation for Control of Diseases by Raspberry Pi 4 and Pi Camera System

Mahadeo Pisal,

Snehal Jadhav,

Urmila Patil

2024

Int Res J Adv Engg Hub

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“…The optimal decomposer and optimizer in the cloud computing infrastructure provide high performance in feature selection. Decomposition in different datasets are performed in papers [18][19][20][21][22][23][24] at cloud storage for an optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Latha¹,

Balaji²,

Bačanin³

et al. 2022

Computer Systems Science and Engineering

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Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user's access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.

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“…To examine the effect of EMF radiations on the morphology of the D. Melanogaster brain, four classifiers were used to segregate it. These four classifiers were naïve Bayes [15][16][17], artificial neural network [18][19][20][21][22], support vector machine [23][24][25], and unsystematic forest.…”

Section: Segregating Open/not Open Groups Using Machine Learningmentioning

confidence: 99%

Machine Learning Technique to Detect Radiations in the Brain

Gothai¹,

Baseera²,

Prabu³

et al. 2022

Computer Systems Science and Engineering

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The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF.Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing.

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Monitoring the Turmeric Finger Disease and Growth Characteristics Using Sensor Based Embedded System —A Novel Method

Cited by 9 publications

References 18 publications

Turmeric Plant Leaf Oversight, Diagnosis and Recommendation for Control of Diseases by Raspberry Pi 4 and Pi Camera System

Turmeric Plant Leaf Oversight, Diagnosis and Recommendation for Control of Diseases by Raspberry Pi 4 and Pi Camera System

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Machine Learning Technique to Detect Radiations in the Brain

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