Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Battle Royale Optimization for Optimal Band Selection in Predicting Soil Nutrients Using Visible and Near-Infrared Reflectance Spectroscopy and PLSR Algorithm

Ramasamy Jagadeeswaran; R. Anand; K. Ranganathan; D. Muthumanickam; Backiyathu Saliha; R. Kumaraperumal; Sathishkumar Samiappan

Journal of Imaging · 2025

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Summary

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 usin

Source type
Peer-reviewed study
DOI
10.3390/jimaging11030083
Catalogue ID
NRmokwjily-002
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