Special lssues
Table of Content

Recent Advances in Deep Learning and Saliency Methods for Agriculture

Submission Deadline: 31 March 2021 (closed)

Guest Editors

Dr. Muhammad Sharif, COMSATS University Islamabad, Pakistan.
Dr. ShuiHua Wang, University of Leicester, UK.
Dr. Muhammad Attique Khan, HITEC University Taxila, Pakistan.

Summary

Health monitoring of plants and fruits is essential for sustainable agriculture. In the agriculture farming business, plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes significant diminution in both quality and capacity of growing crops. Therefore, detection and taxonomy of various plants diseases is crucial, and it demands utmost attention. Whereas, detection of fruit diseases not only helps to avoid the yield loses but also improves the quality of products. The classical method for fruit disease identification is based on visual inspection by agriculture experts but these methods are prone to errors and suffers from high cost and time consumption. Moreover, in some cases visual inspection by experts is not feasible due to presence of crops at distant locations.

Automated detection and identification of plant diseases has got significant research interest in recent years in the domain of computer vision and machine learning applications. Sophisticated image processing coupled with advanced computer vision techniques results such as saliency methods and Deep Learning in accurate and fast identification with less human effort and labor cost. The saliency methods are outperforms for detection of plants and fruits diseases, whereas, the deep learning is one of latest research area of machine learning and achieved significant performance in Agriculture.

The major aim of this issue to provide an efficient solution for both detection and classification of plants and fruits diseases, where researchers in different domains related to deep learning and saliency methods shows their ideas and results.


Keywords

This special issue primarily focused on following topics of agriculture application using saliency approaches and deep learning:
• Processing methods in agriculture based on deep learning
• Detection of crops and fruits diseases using saliency methods
• Convolutional Neural Network based fruits crops diseases detection
• FGPA with saliency approaches for diseases detection
• Recognition of plants and fruits diseases using deep learning
• Classification of plants types using deep learning
• Real Time deep learning based fruit crops diseases Recognition
• FGPA with deep learning for plants and fruits diseases classification
• Features optimization for plants diseases classification
• Fusion of Fully Connected layers for classification of plants diseases
• Selection of optimal features for plants diseases

Published Papers


  • Open Access

    ARTICLE

    CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

    Mehwish Zafar, Javeria Amin, Muhammad Sharif, Muhammad Almas Anjum, Seifedine Kadry, Jungeun Kim
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed,… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

    Javed Rashid, Imran Khan, Irshad Ahmed Abbasi, Muhammad Rizwan Saeed, Mubbashar Saddique, Mohamed Abbas
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

    Shah Faisal, Kashif Javed, Sara Ali, Areej Alasiry, Mehrez Marzougui, Muhammad Attique Khan, Jae-Hyuk Cha
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

  • Open Access

    ARTICLE

    Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

    Huaxin Zhou, Ziying Fang, Yilin Wang, Mengjun Tong
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 175-194, 2023, DOI:10.32604/cmc.2023.037342
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data. First, after comparing various attention… More >

  • Open Access

    ARTICLE

    Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

    Usra Yasmeen, Muhammad Attique Khan, Usman Tariq, Junaid Ali Khan, Muhammad Asfand E. Yar, Ch. Avais Hanif, Senghour Mey, Yunyoung Nam
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3667-3684, 2022, DOI:10.32604/cmc.2022.022264
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning showing significant success in the… More >

  • Open Access

    ARTICLE

    Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

    Ahmed Elaraby, Walid Hamdy, Madallah Alruwaili
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4019-4031, 2022, DOI:10.32604/cmc.2022.022161
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis. The proposed work describes… More >

  • Open Access

    ARTICLE

    An Integrated Deep Learning Framework for Fruits Diseases Classification

    Abdul Majid, Muhammad Attique Khan, Majed Alhaisoni, Muhammad Asfand E. yar, Usman Tariq, Nazar Hussain, Yunyoung Nam, Seifedine Kadry
    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1387-1402, 2022, DOI:10.32604/cmc.2022.017701
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method… More >

  • Open Access

    ARTICLE

    Deep Rank-Based Average Pooling Network for Covid-19 Recognition

    Shui-Hua Wang, Muhammad Attique Khan, Vishnuvarthanan Govindaraj, Steven L. Fernandes, Ziquan Zhu, Yu-Dong Zhang
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2797-2813, 2022, DOI:10.32604/cmc.2022.020140
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract (Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive; community-acquired pneumonia; second pulmonary tuberculosis; and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-conv rank-based average pooling module (NRAPM) was proposed in which rank-based pooling—particularly, rank-based average pooling (RAP)—was employed to avoid overfitting. Third, a novel DRAPNet was proposed… More >

  • Open Access

    ARTICLE

    Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

    Nazar Hussain, Muhammad Attique Khan, Usman Tariq, Seifedine Kadry, MuhammadAsfand E. Yar, Almetwally M. Mostafa, Abeer Ali Alnuaim, Shafiq Ahmad
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3… More >

  • Open Access

    ARTICLE

    Image Segmentation Based on Block Level and Hybrid Directional Local Extrema

    Ghanshyam Raghuwanshi, Yogesh Gupta, Deepak Sinwar, Dilbag Singh, Usman Tariq, Muhammad Attique, Kuntha Pin, Yunyoung Nam
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3939-3954, 2022, DOI:10.32604/cmc.2022.018423
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract In the recent decade, the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities. Image segmentation is a key step in digitalization. Segmentation plays a key role in almost all areas of image processing, and various approaches have been proposed for image segmentation. In this paper, a novel approach is proposed for image segmentation using a nonuniform adaptive strategy. Region-based image segmentation along with a directional binary pattern generated a better segmented image. An adaptive mask of 8 × 8 was circulated over the pixels whose bit value was 1 in the… More >

  • Open Access

    ARTICLE

    A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

    Faiz Ali Shah, Muhammad Attique Khan, Muhammad Sharif, Usman Tariq, Aimal Khan, Seifedine Kadry, Orawit Thinnukool
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1491-1507, 2022, DOI:10.32604/cmc.2022.019490
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms of different fruit diseases and… More >

  • Open Access

    ARTICLE

    Classification of Citrus Plant Diseases Using Deep Transfer Learning

    Muhammad Zia Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, Usman Tariq, Sajjad Shaukat Jamal, Jawad Ahmad, Iqtadar Hussain
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1401-1417, 2022, DOI:10.32604/cmc.2022.019046
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning… More >

  • Open Access

    ARTICLE

    Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

    Javaria Amin, Muhammad Almas Anjum, Muhammad Sharif, Seifedine Kadry, Yunyoung Nam
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 619-635, 2022, DOI:10.32604/cmc.2022.018562
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore,… More >

  • Open Access

    ARTICLE

    Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

    Muhammad Rizwan Latif, Muhamamd Attique Khan, Muhammad Younus Javed, Haris Masood, Usman Tariq, Yunyoung Nam, Seifedine Kadry
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their… More >

  • Open Access

    ARTICLE

    Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

    Rabia Saleem, Jamal Hussain Shah, Muhammad Sharif, Ghulam Jillani Ansari
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3581-3601, 2021, DOI:10.32604/cmc.2021.017700
    (This article belongs to this Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed FrCNnet directly learns the features… More >

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