{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T18:04:25Z","timestamp":1774202665978,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2013Flame 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.<\/jats:p>","DOI":"10.3390\/jimaging11030083","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T11:04:22Z","timestamp":1742209462000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Battle Royale Optimization for Optimal Band Selection in Predicting Soil Nutrients Using Visible and Near-Infrared Reflectance Spectroscopy and PLSR Algorithm"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8658-9260","authenticated-orcid":false,"given":"Jagadeeswaran","family":"Ramasamy","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and GIS, CWGS TamilNadu Agricultural University, Coimbatore 641003, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1558-4577","authenticated-orcid":false,"given":"Anand","family":"Raju","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"given":"Kavitha","family":"Krishnasamy Ranganathan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Sona College of Technology, Salem 636005, India"}]},{"given":"Muthumanickam","family":"Dhanaraju","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, CWGS TamilNadu Agricultural University, Coimbatore 641003, India"}]},{"given":"Backiyathu","family":"Saliha","sequence":"additional","affiliation":[{"name":"Agricultural Research Station, TamilNadu Agricultural University (TNAU), Kovilpatti 628502, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6659-9587","authenticated-orcid":false,"given":"Kumaraperumal","family":"Ramalingam","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, CWGS TamilNadu Agricultural University, Coimbatore 641003, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8443-883X","authenticated-orcid":false,"given":"Sathishkumar","family":"Samiappan","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering & Soil Science, University of Tennessee at Knoxville, Knoxville, TN 37996, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid-infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_2","unstructured":"Liu, X., Li, Z., and He, Y. 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