解决农业挑战:火龙果病害识别的最佳特征选择技术的确定

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2023-11-02 DOI:10.1016/j.array.2023.100326
Rashiduzzaman Shakil , Shawn Islam , Yeasir Arafat Shohan , Anonto Mia , Aditya Rajbongshi , Md Habibur Rahman , Bonna Akter
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引用次数: 0

摘要

火龙果是全球农业的重要原料。尽管如此,它越来越受欢迎,并且由于其许多健康益处,在资源贫乏、环境退化的地区是一种可行的解决办法。然而,许多火龙果种植园受到这种疾病的影响,产量下降,检测系统仍然是传统的。农民缺乏疾病识别和管理专业知识,降低了作物质量和产品。因此,很少进行研究以协助需要充分农业支助的特定农民。本研究提出了一种基于农业的龙病自主识别系统,该系统采用深度分析特征选择技术。在采集到龙的实时图像后,使用各种图像处理技术对图像进行预处理。分割后提取两个重要特征。采用方差分析(ANOVA)和最小绝对收缩和选择算子(LASSO)作为特征选择技术,基于互分评估特征等级。为了分析所使用的机器学习算法的有效性,将六个不同的机器学习分类器应用于排名靠前的特征集,并使用七个不同的性能评估指标来衡量它们的性能。通过对基于ANOVA和LASSO特征集的分类器进行比较,AdaBoost和Random Forest分类器对LASSO特征排序方法的准确率最高,为96.29%。尽管如此,我们已经为LASSO特征集优化了每个分类器的计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition

Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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