Plant diseases appear to have become a major threat to global food security, both in terms of production and supply. In this paper, we present a real-time plant disease that relies on altered deep convolutional neural networks. The plant illness images first were expanded by image processing technologies, resulting in the plant disease sets of data. A Wolf Optimization with Faster Region-based Convolutional Neural Network (WO-FRCNN) system that improved removal characteristics was used to identify plant diseases. The proposed method improved the detection of plant diseases and achieved a precision of 96.32%. Prevention activities achieve the basic rate of 15.01 FPS as the existing methods according to experimental data. This study means that the real detectors Improved WO-FRCNN, which would depend on deep learning. It would be a viable option for diagnosing plant diseases and used for identifying other diseases within plants. The evaluation report indicates that the proposed method provides good reliability.
CITATION STYLE
Prabu, M., & Chelliah, B. J. (2023). Develop Hybrid Wolf Optimization with Faster RCNN to Enhance Plant Disease Detection Performance Analysis. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 163, pp. 243–253). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-0609-3_17
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