利用可见光和近红外反射光谱和PLSR算法预测土壤养分的最佳波段选择的Battle Royale优化。

IF 2.7 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Jagadeeswaran Ramasamy, Anand Raju, Kavitha Krishnasamy Ranganathan, Muthumanickam Dhanaraju, Backiyathu Saliha, Kumaraperumal Ramalingam, Sathishkumar Samiappan
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引用次数: 0

摘要

尝试利用高光谱遥感技术和机器学习算法对土壤特性进行量化。共收集了 100 份代表不同地点和土壤养分状况的土壤样本,并按照标准方法分析了土壤 pH 值、导电率、土壤有机碳、可利用氮(AN)、可利用磷(AP)和可利用钾(AK)。土壤的性质范围很广,即 pH 值从 5.62 到 8.49,EC 值从 0.08 到 1.78 dS/m,土壤有机碳从 0.23 到 0.94%,可利用氮从 154 到 344 千克/公顷,可利用磷从 9.5 到 25.5 千克/公顷,可利用钾从 131 到 747 千克/公顷。使用 SVC GER 1500 光谱辐射计(光谱范围:350 至 1050 纳米)对同一组土壤样本进行了光谱反射率测量。对测量到的各种土壤的光谱特征进行整理,以建立光谱库,并推导出各种光谱指数,将其与土壤特性相关联,从而量化养分。土壤样本按 60:40 的比例分别用于训练和验证。为了从土壤光谱中选择最佳波段(波长),我们采用了元启发式算法,即粒子群优化(PSO)、飞蛾-火焰优化(MFO)、授粉优化(FPO)和大逃杀优化(BRO)算法。此外,还使用偏最小二乘法回归(PLSR)来寻找潜在变量,并评估各种算法在预测土壤特性方面的性能。结果表明,通过 Battle Royale 优化技术,可以从光谱反射测量中量化养分,其准确性从一般到良好,pH 值、EC 值、土壤有机碳、可利用氮、可利用磷和可利用钾的 R2 值分别为 0.45、0.32、0.48、0.21、0.71 和 0.35。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Battle Royale Optimization for Optimal Band Selection in Predicting Soil Nutrients Using Visible and Near-Infrared Reflectance Spectroscopy and PLSR Algorithm.

An attempt was made to quantify soil properties using hyperspectral remote-sensing techniques and machine-learning algorithms. In total, 100 soil samples representing various locations and soil-nutrient statuses were collected, and the samples were analyzed for soil pH, EC, soil organic carbon, available nitrogen (AN), available phosphorus (AP), and available potassium (AK) by following standard methods. Soil had a wide range of properties, i.e., pH varied from 5.62 to 8.49, EC varied from 0.08 to 1.78 dS/m, soil organic carbon varied from 0.23 to 0.94%, available nitrogen varied from 154 to 344 kg/ha, available phosphorus varied from 9.5 to 25.5 kg/ha, and available potassium varied from 131 to 747 kg/ha. The same set of soil samples were subjected to spectral reflectance measurement using SVC GER 1500 Spectroradiometer (spectral range: 350 to 1050 nm). The measured spectral signatures of various soils were organized for developing a spectral library and for deriving various spectral indices to correlate with soil properties to quantify the nutrients. The soil samples were partitioned into 60:40 ratios for training and validation, respectively. In order to select optimum bands (wavelength) from the soil spectra, we have employed metaheuristic algorithms i.e., Particle Swarm Optimization (PSO), Moth-Flame optimization (MFO), Flower Pollination Optimization (FPO), and Battle Royale Optimization (BRO) algorithm. Further partial least square regression (PLSR) was used to find the latent variable and to evaluate various algorithms for their performance in predicting soil properties. The results indicated that nutrients could be quantified from spectral reflectance measurement with fair to good accuracy through the Battle Royale Optimization technique with a R2 value of 0.45, 0.32, 0.48, 0.21, 0.71, and 0.35 for pH, EC, soil organic carbon, available-N, available-P, and available-K, respectively.

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来源期刊
Journal of Imaging
Journal of Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.90
自引率
6.20%
发文量
303
审稿时长
7 weeks
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