Laurent series intelligent multidimensional object optimization classification for crop disease detection

Abstract

Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively.

Plant phenotyping relevance

イネ病害の画像セグメンテーション、特徴抽出、分類を中心とする計算的な病害状態推定手法を開発・評価しており、植物フェノタイピング手法が研究の中核である。

Code and data availability

The paper's rice crop disease image dataset is publicly available via three author-deposited Figshare datasets (smut diseases, blast disease, bacterial blight) cited as references [11]-[13] and used in Table 1. No code or model availability is stated; other data is only on request.

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