An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Gashaw Ayalew , Qamar Uz Zaman , Arnold W. Schumann , David C. Percival , Young Ki Chang
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引用次数: 2

Abstract

A Gabor wavelets based technique was investigated as a potential tool for scene classification (into one of bare patch, plant, or weed) for its ultimate utility in site-specific application of agrochemicals in wild blueberry fields.

Images were gathered from five sites located in central Nova Scotia, Canada. Gabor wavelet features extracted from these images were used to classify scenes according to visually determined classes using step-wise linear discriminant analysis.

For individual fields, classification accuracy attained ranged between 87.9% and 98.3%; selected Gabor features ranged between 27 and 72; contextual accuracy for herbicide ranged between 67.5% and 96.7%, and contextual accuracy for fertilizer ranged between 63.6% and 97.1%. The pooled scenes yielded a classification accuracy of 81.4%, and contextual accuracy figures of 61.1% and 73.1% for herbicide and fertilizer, respectively, with selected Gabor features of 36.

Calibrations based on LDA coefficients from the pooled scenes could help avoid the need to re-calibrate for each field, whereas those based on individual field LDA coefficients could improve accuracy, hence enable saving on expensive agrochemicals.

Gabor小波特征在野生蓝莓田场景分类中的潜力研究
研究了一种基于Gabor小波的场景分类(光斑、植物或杂草)的潜在工具,并将其最终应用于野生蓝莓田间农药的具体应用。这些图像是从加拿大新斯科舍省中部的五个地点收集的。从这些图像中提取Gabor小波特征,使用逐步线性判别分析,根据视觉确定的类别对场景进行分类。对于单个领域,获得的分类准确率在87.9%到98.3%之间;选定的Gabor特征在27到72之间;除草剂的上下文准确度在67.5% ~ 96.7%之间,化肥的上下文准确度在63.6% ~ 97.1%之间。在选择Gabor特征为36的情况下,混合场景对除草剂和肥料的分类准确率分别为81.4%和61.1%和73.1%。基于混合场景的LDA系数的校准可以帮助避免为每个领域重新校准的需要,而基于单个领域LDA系数的校准可以提高准确性,从而节省昂贵的农用化学品。
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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