WITHDRAWN: Recognition of Apple Leaf Diseases using Deep Learning and Variances-Controlled Features Reduction

Tahir, Muqadas Bin; Khan, Muhammad Attique; Javed, Kashif; Kadry, Seifedine; Zhang, Yudong; Akram, Tallha; Nazir, Muhammad

doi:10.1016/j.micpro.2021.104027

Cited by 49 publications

(28 citation statements)

References 50 publications

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“…In transfer learning, an existing CNN model for accomplishing a task is utilized for another study. In [120][121][122], researchers presented the concept of disease identification from plant leaf images using transfer learning. Sravan et al [120] demonstrated the fine-tuning of hyperparameters of existing ResNet50 for disease classification and achieved a higher accuracy of 99.26% on the PlantVillage data set containing 20,639 images.…”

Section: Analysis Of Dcnns For Plant Leaf Disease Identificationmentioning

confidence: 99%

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A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

202

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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Section: Analysis Of Dcnns For Plant Leaf Disease Identificationmentioning

confidence: 99%

“…On the other hand, Tahir et al [ 122 ] investigated disease identification from the Apple plant using InceptionV3 and shown an overall accuracy of 97% on the PlantVillage data set. Shin et al [ 123 ] depicted the comparative study of six different CNN models to identify powdery mildew disease on strawberry leaves.…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

202

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“…In this condition, the output of the layers is used as the demanded image feature vector. At last, the image feature vector is compared with the feature matrix, and the deep neural network uses some layers for understanding some parts of data; however, for the classification of data, we should have a collect of probabilities for the final decision [15]. Softmax is a popular function that is used for normalizing the probability values in a standard range (0 to 1).…”

Section: Image Level Balancingmentioning

confidence: 99%

A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Nanehkaran

Fard

2021

Computational Intelligence and Neuroscience

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Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.

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“…As above discussed literature, it is obvious that [43] computer vision is playing vital role in agriculture for early detection of fruit and leaf diseases [44] . In [45] apple disease namely apple scab, brown spot and apple cedar were correctly recognized using the deep learning inception V3 model and overall 97% accuracy was achieved. For this study, we did not employ deep learning models due to usage of small and trivial dataset of rice varieties and conventional machine learning model provided quite satisfactory results in term of accuracy, time and space as compared to deep learning models [46] .…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Approach for Classification of Rice Varieties Using Texture Features

Qadri

Aslam

Nawaz

et al. 2021

International Journal of Food Properties

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The main objective of this study was to assess the machine vision (MV) techniques to classify six Asian rice varieties commonly named as Kachi-Kainat, Kachi-Toota, Kainat-Pakki, Super-Basmati-Kachi, Super-Basmati-Pakki, and Super-Maryam-Kainat (A1, A2, A3, A4, A5, and A6), mainly cultivated in Pakistan, China, India, Bangladesh, and neighboring countries. The sample of each selected rice variety contained 1800 grains, giving a total of 10800 (1800 × 6) grain samples. A cell phone camera captured the actual field digital images dataset in an open climate. All the captured images were enhanced and converted into the standard 8-bit gray-scale format. Six radius-based nonoverlapping regions of interest (ROI's) were taken on each captured image inducing a total of 3600 (6 × 600) ROI's image dataset. We have extracted Binary (B), Histogram (H), and Texture (T) features from each image. We converted these forty-three features for each image into 154800 (43 × 3600) feature vector (FV) space to discriminate rice varieties. After optimizing the FV, five MV classifiers, namely; LMT Tree (LMT-T), Meta Classifier via Regression (MCR), Meta Bagging (MB), Tree J48 (T-J48), and Meta Attribute Select Classifier (MAS-C), were deployed attaining the classification accuracies as 97.4%, 97.0%, 96.3%, 95.74%, and 95.2%, respectively. The maximum overall accuracy (MOA) observed was 97.4% by LMT-Tree.

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WITHDRAWN: Recognition of Apple Leaf Diseases using Deep Learning and Variances-Controlled Features Reduction

Cited by 49 publications

References 50 publications

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Machine Vision Approach for Classification of Rice Varieties Using Texture Features

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