Feature aggregation for nutrient deficiency identification in chili based on machine learning

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Deffa Rahadiyan , Sri Hartati , Wahyono , Andri Prima Nugroho
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引用次数: 1

Abstract

Macronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision. This study uses 5166 image data after augmentation process for six plant health conditions. But the analysis of one feature cannot represent plant health condition. Therefore, a careful combination of features is required. This study combines three types of features with HSV and RGB for color, GLCM and LBP for texture, and Hu moments and centroid distance for shapes. Each feature and its combination are trained and tested using the same MLP architecture. The combination of RGB, GLCM, Hu moments, and Distance of centroid features results the best performance. In addition, this study compares the MLP architecture used with previous studies such as SVM, Random Forest Technique, Naive Bayes, and CNN. CNN produced the best performance, followed by SVM and MLP, with accuracy reaching 97.76%, 90.55% and 89.70%, respectively. Although MLP has lower accuracy than CNN, the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.

基于机器学习的辣椒营养缺乏识别特征聚合
大量营养素缺乏会抑制辣椒植物的生长发育。在基于特定特征处理植物图像数据方面发挥作用的非破坏性方法之一是计算机视觉。这项研究使用了5166个植物健康状况增强过程后的图像数据。但是,对一个特征的分析不能代表植物的健康状况。因此,需要对功能进行仔细组合。这项研究结合了三种类型的特征:颜色的HSV和RGB,纹理的GLCM和LBP,形状的Hu矩和质心距离。每个特征及其组合都使用相同的MLP体系结构进行训练和测试。RGB、GLCM、Hu矩和质心距离特征的组合产生最佳性能。此外,本研究将MLP架构与先前的研究(如SVM、随机森林技术、朴素贝叶斯和CNN)进行了比较。CNN表现最好,其次是SVM和MLP,准确率分别达到97.76%、90.55%和89.70%。尽管MLP的准确性低于CNN,但用于识别植物健康状况的模型在简单的农业环境中具有相当好的成功率。
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
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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