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
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