Special Issue "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
  • Deep Rank-Based Average Pooling Network for Covid-19 Recognition
  • 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
  •   Views:45       Downloads:22        Download PDF

  • Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection
  • 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
  •   Views:42       Downloads:22        Download PDF

  • Image Segmentation Based on Block Level and Hybrid Directional Local Extrema
  • 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
  •   Views:57       Downloads:27        Download PDF

  • A Cascaded Design of Best Features Selection for Fruit Diseases Recognition
  • 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
  •   Views:201       Downloads:114        Download PDF

  • Classification of Citrus Plant Diseases Using Deep Transfer Learning
  • 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
  •   Views:184       Downloads:162        Download PDF

  • Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks
  • 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
  •   Views:164       Downloads:125        Download PDF

  • Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm
  • 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
  •   Views:368       Downloads:197        Download PDF

  • Mango Leaf Disease Identification Using Fully Resolution Convolutional Network
  • 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
  •   Views:264       Downloads:160        Download PDF