Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/4731
Title: PESTICIDE RECOMMENDATION FOR DIFFERENT LEAF DISEASES AND RELATED PESTS USING MULTI-DIMENSIONAL FEATURE LEARNING DEEP CLASSIFIER
Authors: Jaithoon Bibi Mohammed, Saleem
Karpagavalli, Shanmugam
Keywords: leaf diseases
PDATFEGAN
MFL-DCNN
pesticide
fuzzy rule
rough set
intuitionistic fuzzy approximation space
recommendation system
Issue Date: 28-Feb-2023
Publisher: International Information and Engineering Technology and Association
Abstract: In 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.
URI: https://doi.org/10.18280/isi.280113
Appears in Collections:2.Article (95)



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