Abstract
To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework’s experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.
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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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We thank the anonymous referees for their useful suggestions.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by D. Lita Pansy and Dr. M. Murali. The first draft of the manuscript was written by D. Lita Pansy, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Pansy, D.L., Murali, M. UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN. Environ Monit Assess 195, 1120 (2023). https://doi.org/10.1007/s10661-023-11678-9
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DOI: https://doi.org/10.1007/s10661-023-11678-9