Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-08-30 DOI:10.1007/s13198-024-02486-6
Shashank Chaudhary, Upendra kumar
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Abstract

The timely detection and identification of crop diseases is a crucial aspect of the agricultural sector. It contributes significantly to the by and large productivity of the plant. One of the most crucial factors that we need to consider while determining a plant’s susceptibility to a particular disease is the visual characteristics of the affected plant. The increasing popularity of automation and availability of efficient techniques for disease identification has led to the development of novel methods and engraved impactful technologies in field of automated disease detection. The traditional methods have not been able to provide the researchers with the most accurate results. The proposed model in this work can identify the rice crop disease without relying on subjective data and have many advantages over traditional approaches as evident from the results derived. It has the potential to improve the efficiency of the process and aid in early detection. Machine learning method presents real-time automated decision support systems and can help improve crop or plant growth productivity and quality. This work aims to introduce a new and enhanced method as Neuro-GA, which is a combination of both the artificial neural network (ANN) and the genetic algorithm (GA). It has been claimed that it is more powerful and accurate than the traditional methods. The pioneer and nascent stages of this analysis includes preprocessing of the data was carried out. The features were then extracted using Gray-level co-occurrence matrix (GLCM) and subsequently the finally extracted features were cascaded to the Neuro-GA classifier. The digital image processing (DIP) techniques used in this study for rendering visual images along with Neuro-GA classifier resulted in skyrocket accuracy level of 90% and above. The technique validated in this study has allowed the automated monitoring of various aspects of crop production and farming and an omnipotent promising efficiency hence this approach can be magnanimously effective in monitoring agricultural production and thereby plummeting waste allied with crop damage.

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利用灰度共现矩阵 (GLCM) 和神经-GA 分类器识别水稻作物病害
及时发现和识别作物病害是农业部门的一个重要方面。它在很大程度上有助于提高植物的产量。在确定植物对特定病害的易感性时,我们需要考虑的最关键因素之一是受影响植物的视觉特征。随着自动化的日益普及和高效病害识别技术的出现,自动病害检测领域出现了许多新方法和有影响力的技术。传统方法无法为研究人员提供最准确的结果。与传统方法相比,这项工作中提出的模型无需依赖主观数据就能识别水稻作物病害,从得出的结果来看,它具有许多优势。它具有提高工作效率和帮助早期检测的潜力。机器学习方法提供了实时自动决策支持系统,有助于提高作物或植物生长的生产力和质量。这项工作旨在引入一种新的增强型方法,即神经-遗传算法(Neuro-GA),它是人工神经网络(ANN)和遗传算法(GA)的结合。据称,它比传统方法更强大、更准确。这项分析的先驱和初级阶段包括对数据进行预处理。然后使用灰度级共现矩阵(GLCM)提取特征,最后将提取的特征级联到神经-GA 分类器。本研究中用于呈现视觉图像的数字图像处理(DIP)技术和神经-GA 分类器的准确率高达 90% 及以上。本研究中验证的技术可以对作物生产和耕作的各个方面进行自动监测,而且效率极高,因此这种方法在监测农业生产方面非常有效,从而大大减少了与作物损害相关的浪费。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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