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.


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.

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
  • 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:194       Downloads:107        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:157       Downloads:121        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:153       Downloads:114        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:358       Downloads:185        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:252       Downloads:151        Download PDF