Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Yasmeen, Usra; Khan, Muhammad Attique; Tariq, Usman; Khan, Junaid Ali; Asfand-e-yar, Muhammad; Hanif, Ch. Avais; Mey, Senghour; Nam, Yunyoung

doi:10.32604/cmc.2022.022264

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Individual filters with LDPC code mitigated impulsive noise in OFDM better than the proposed technique. In the future, this technique can be applied for several computer vision research applications such as medical [27,28], agriculture [29,30], surveillance, and many more [31][32][33][34][35][36].…”

Section: Resultsmentioning

confidence: 99%

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

Irum¹,

Fayyaz²,

Ayub³

et al. 2023

Computer Systems Science and Engineering

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An effective communication application necessitates the cancellation of Impulsive Noise (IN) from Orthogonal Frequency Division Multiplexing (OFDM), which is widely used for wireless applications due to its higher data rate and greater spectral efficiency. The OFDM system is typically corrupted by Impulsive Noise, which is an unwanted short-duration pulse with random amplitude and duration. Impulsive noise is created by humans and has non-Gaussian characteristics, causing problems in communication systems such as high capacity loss and poor error rate performance. Several techniques have been introduced in the literature to solve this type of problem, but they still have many issues that affect the performance of the presented methods. As a result, developing a new hybridization-based method is critical for accurate method performance. In this paper, we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM. The proposed method is also compared to Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Recursive Least Square (RLS) adaptive filters. It has also been tested using the binary phase-shift keyed (BPSK), four quadrature amplitude modulation (QAM), sixteen QAM, and thirty-two QAM modulation techniques. Bit error Rate (BER) simulations are used to evaluate system performance, and improved performance is obtained. Furthermore, the proposed method is more effective than recent methods.

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Section: Resultsmentioning

confidence: 99%

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

Irum¹,

Fayyaz²,

Ayub³

et al. 2023

Computer Systems Science and Engineering

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“…ResNet-18 has an eighteen-layer deep neural network framework that utilises an improved form of Genetic Algorithm for determining the characteristics. Additionally, it adds the concept of residual learning [20]. The best way to choose features is to cut down on the time it takes to do calculations.…”

Section: Methods For Extracting the Features And Selecting The Featuresmentioning

confidence: 99%

“…[19] made up a unique pattern by the pictures have been read, random 256x256-pixel pieces are cut out and then image filtering, distortion, flip, or rotation effects are added. [20] Each pixel in a certain area is the same in terms of things that can be measured, like colour, brightness, or structure. These features are enhanced by histogram equalization and thresholding on the grayscale images.…”

Section: Pre-processing Techniques For Image Intensificationmentioning

confidence: 99%

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Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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“…Fruits play an important role in human nutrition as sources of vitamins, minerals, and dietary fibers [4]. Furthermore, fruits and vegetables contain 16% magnesium, 19% iron, and 9 percent calories [5].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Rehman¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Identifying fruit disease manually is time-consuming, expertrequired, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.

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Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Cited by 12 publications

References 29 publications

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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