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Utilizing chaos equations and fractal dimensions to differentiate various crop types

机译：利用混沌方程和分形维数来区分各种作物类型

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Chaos theory is a method used in both qualitative and quantitative analysis for exploring the behavior of dynamic systems that can only be predicted using overall and continuous data relationships instead of single data relationships. A chaotic algorithm requires entry of a series of data. Obtaining continuous data from current commercial satellites or aerial images is difficult because of climate conditions and budget constraints. However, hyperspectral ground images captured by portable devices exhibit numerous wavebands and narrow spectral ranges. Spectral ranges that are similar form continuous data. Spectral information in hyperspectral images is richer than that in ordinary multispectral images, rendering them useful for detecting minor spectral differences. Such differences overcome the insufficiency in multispectral images. Portable spectroradiometers can be used to measure the on-site reflectance curves of various land features at various times, locations, and statuses. Because crops change continuously and rapidly according to temporospatial conditions (e.g., etiolation, abscission, disease, unevenly distributed spatial density, and differences in planting times), a considerable number of variables are added to crop spectra, which is the primary cause of difficulty in classifying crops. Chaotic algorithms may be suitable for solving the crop classification problem and effectively identify various crop categories. The research sample investigated in the present study comprised garlic, scallion, sweet potato, and carrots, most of which are planted in Yunlin County, Taiwan and are easy to sample. The chaotic image features of spectral reflectance of the various crops were captured at the same time and compared to discern any differences. MATLAB was used to conduct the chaos simulation and to calculate the fractal dimensions.
机译：混沌理论是一种用于定性和定量分析的方法，用于探索动态系统的行为，该行为只能使用整体和连续数据关系而不是单个数据关系来预测。混沌算法需要输入一系列数据。由于气候条件和预算限制，很难从当前的商业卫星或航空图像中获取连续数据。但是，便携式设备捕获的高光谱地面图像表现出无数个波段和狭窄的光谱范围。相似的光谱范围形成连续数据。高光谱图像中的光谱信息比普通的多光谱图像中的光谱信息丰富，从而使其可用于检测微小光谱差异。这样的差异克服了多光谱图像的不足。便携式光谱辐射仪可用于在不同时间，位置和状态下测量各种陆地特征的现场反射率曲线。由于农作物会根据颞pat条件（例如黄化，脱落，病害，空间密度分布不​​均匀以及播种时间的差异）而连续快速变化，因此在农作物光谱中添加了大量变量，这是造成农作物生长困难的主要原因。对农作物进行分类。混沌算法可能适合解决农作物分类问题并有效地识别各种农作物类别。本研究调查的研究样本包括大蒜，葱，红薯和胡萝卜，其中大部分种植在台湾云林县，且易于采样。同时捕获各种作物的光谱反射率的混沌图像特征，并进行比较以识别任何差异。使用MATLAB进行混沌模拟并计算分形维数。

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