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dc.contributor.authorSaleem, Jaithoon Bibi Mohammed-
dc.contributor.authorShanmugam, Karpagavalli-
dc.date.accessioned2024-10-01T06:06:51Z-
dc.date.available2024-10-01T06:06:51Z-
dc.date.issued2023-02-
dc.identifier.issn16331311-
dc.identifier.urihttps://doi.org/10.18280/isi.280113-
dc.description.abstractIn agricultural applications, the most essential task is to classify leaf diseases and their associated pests from various aspects. To achieve this, a Deep Convolutional Neural Network (DCNN) model was developed to classify the leaf diseases based on the soil and climatic features. But it needs a recommendation system to control the pesticide use for controlling the leaf diseases caused by specific pests. Hence, this paper hybridizes the Multi-dimensional Feature Learning-based DCNN (MFL-DCNN) with the Rough Set (RS) on an intuitionistic Fuzzy approximation space (RSF)-based decision support system to suggest the proper pesticides for a certain crop to be planted in a particular region. First, the leaf images are augmented by the Positional-aware Dual-Attention and Topology-Fusion with Evolutionary Generative Adversarial Network (PDATFEGAN) model. Then, the multi-dimensional data such as the created leaf images, pest, soil, weather, and pesticide data are fed to the DCNN with a softmax classifier for classifying leaf diseases and related pests. Then, the RSF-based decision model is applied, which determines the correlation between leaf disease and pests to recommend suitable pesticides. Finally, the experimental results reveal that the MFL-DCNN-RSF accomplishes a maximum efficiency than all other models for recommending pesticides to control leaf diseases and pests.en_US
dc.language.isoen_USen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.subjectleaf diseasesen_US
dc.subjectPDATFEGANen_US
dc.subjectMFL-DCNNen_US
dc.subjectpesticideen_US
dc.subjectfuzzy ruleen_US
dc.subjectrough seten_US
dc.subjectintuitionistic fuzzy approximation spaceen_US
dc.subjectrecommendation systemen_US
dc.titlePESTICIDE RECOMMENDATION FOR DIFFERENT LEAF DISEASES AND RELATED PESTS USING MULTI-DIMENSIONAL FEATURE LEARNING DEEP CLASSIFIER (Article)en_US
dc.typeArticleen_US
Appears in Collections:b) 2023-Scopus Open Access (Pdf)



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