Climate-Induced Variations in Plant Leaf Diseases: Optimized Detection Using Honey Badger Algorithm

Abstract

Climate fluctuation can have positive, negative, or neutral effects on plant leaf diseases depending on the location and period. It can also increase, decrease, or have no influence at all. These impacts will also be observed on other organisms, plant leaves, and other components of the agro-ecosystem. However, identifying these consequences is challenging, thus specialists from other disciplines need to look outside their own academic purview and consider the effects of climate fluctuation in a broader context. The principal application of this research is the detection of numerous factors related to how plant diseases are affected by climate change. Using data from prediction techniques of the future climate associated with disease, new research used to estimate the distribution in future climatic scenarios are outlined. A historical overview of the temporal and geographical distribution of diseases is provided. Major aspects of the development of illnesses under various atmospheric gas conditions are addressed in terms of the effects of increasing gas concentrations. The Plant Village data,where the data are first collected. To offer a reliable technique for background removal and integration of additional data sets, like geography, illness incidence history, and meteorological conditions, bilateral filtering is employed. The SURF method is employed to obtain attributes from images. Additionally, segmentation is done by K-means clustering method. IFFBPNN operates a realistic plant disease detection system that may be used in a range of climates and geographical locations. To identify the best accuracy, IFFBPNN uses a well-performing nature-inspired optimization technique called HBA to optimize its parameters. The outcomes of the simulations show that the suggested strategy is excellent compared to other modern techniques.

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