Open AccessRecognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture
Author(s) -
Rehman Zia ur,
Khan Muhammad Attique,
Ahmed Fawad,
Damaševičius Robertas,
Naqvi Syed Rameez,
Nisar Wasif,
Javed Kashif
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12183
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , subspace topology , classifier (uml) , feature extraction , discriminant , identification (biology) , support vector machine , entropy (arrow of time) , machine learning , physics , quantum mechanics , botany , biology
Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.