外文摘要：Plant leaf disease identification and classification are the most essential and demanding tasks in the agriculture field. In traditional researches, various automated detection technologies have been developed with the goal of more accurately identifying plant leaf disease. Nevertheless, it faces some problems related to complex mathematical modeling, increased time consumption, processing overhead, and mis-prediction results. Therefore, a novel probabilistic intermittent neural network and artificial jelly fish optimization-based plant leaf disease detection system is proposed in this paper. The proposed work aims to "make a new detection scheme to identify correctly plant leaf disease from the given dataset." Here, the probabilistic intermittent neural network (PINN) classification technique is used to predict label as normal or affected by disease. If it is disease affected, the residual multi-scale Unet segmentation (RMUNet) segmentation technique is applied to segment the disease affected region. Finally, the simulation outcomes confirm the efficiency of the proposed leaf disease identification system under some variables.
外文关键词：Plant leaf disease detection;Kuan-filtered Hough transformation (KHT);Multi-level feature extraction (MLFE);Probabilistic intermittent neural network (PINN);Artificial jelly fish optimization (AJFO);And machine learning
作者：Saraswathi, E;Banu, J Faritha
作者单位：SRM Inst Sci & Technol Ramapuram
期刊名称：JOURNAL OF PLANT DISEASES AND PROTECTION
期刊影响因子：0.0
出版年份：2024
出版刊次：131（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台