BUBMO-based Bi-GRU-CNN model for crop classification with improved feature set: A bigdata perspective.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2025-06-02 DOI:10.1080/0954898X.2025.2503791

Shivi Sharma, D D Sharma, Ashish Sharma, Munish Manas

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引用次数: 0

Abstract

Purpose: Big Data's extensive capabilities can aid in addressing the unpredictability of food supply caused by a variety of issues including soil degradation, climate change, water pollution, socio-cultural expansion, governmental laws, and market volatility. However, crop monitoring and classification are critical components of agricultural precision farming. This paper intends to propose a crop classification via a hybrid classification model.

Design: First, the input image dataset is subjected to the preprocessing stage to enhance the image dataset by removing noise and blurring the edges with the aid of Gaussian filtering. Second, the improved spider local image feature, median binary pattern and haralick texture features are extracted from the preprocessed image dataset by utilizing the map-reduce framework, to handle big data. Third, the hybrid classification model is proposed that involves two classifiers such as Bi-GRU and CNN.

Findings: The weights of both classifier Bi-GRU and CNN were tuned optimally by the proposed hybrid optimization BUBMO that combined both BMO and BWO. The greatest MCC obtained by the propose is 91.47%, whilst the traditional model scored the lowest MCC.

Originality: The accuracy and improved efficacy of the crop categorization are achieved by employing the suggested classification method.

查看原文本刊更多论文

基于bubmo改进特征集的Bi-GRU-CNN作物分类模型：大数据视角。

目的：大数据的广泛能力可以帮助解决由土壤退化、气候变化、水污染、社会文化扩张、政府法律和市场波动等各种问题引起的粮食供应不可预测性。然而，作物监测和分类是农业精准农业的关键组成部分。本文提出了一种基于混合分类模型的农作物分类方法。设计：首先，对输入的图像数据集进行预处理，通过高斯滤波去除噪声和模糊边缘来增强图像数据集。其次，利用map-reduce框架从预处理后的图像数据集中提取改进的蜘蛛局部图像特征、中值二值模式和哈拉里克纹理特征，进行大数据处理；第三，提出了包含Bi-GRU和CNN两个分类器的混合分类模型。结果：提出的结合BMO和BWO的混合优化BUBMO对分类器Bi-GRU和CNN的权重都进行了最优调整。该模型的最大MCC值为91.47%，而传统模型的MCC值最低。独创性：采用本文提出的分类方法，提高了作物分类的准确性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.