• Gugilla.Swathi
• P.Nithin
• D .Narendra
• M.Prudvi raj
• Suneetha

Detailed soil nutrients are important to identify the areas of fertility constraints and assist farmers on agricultural management measures for better crop productivity. Variations in soil physical and chemical properties can affect agricultural productivity and ecosystem. These variations are present and may be important at regional, field, and sub-field (precision agriculture) scales. As traditional methods are time-consuming and expensive, reflectance-based estimates of soil properties such as texture, organic matter content, moisture content, and nutrient status are effective and easily optioned. Soil properties have been related to reflectance measured with laboratory, infield, airborne, and satellite sensors. Both multispectral and hyperspectral instruments have been used, with natural and artificial illumination. Varying levels of accuracy have been obtained, with the best results (r2>0.95) using hyperspectral reflectance data to estimate soil parameters. These parameters are estimated by using machine lerning algorithems such as support vector machine (SVM), Random forest(RF).

precision agriculture, soil fertility index, support vector machine (SVM), Random Forest (RF).

"Soil parameters assessment by using digital images ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.c639-c645, May-2023, Available :http://www.jetir.org/papers/JETIR2305294.pdf

"Soil parameters assessment by using digital images ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppc639-c645, May-2023, Available at : http://www.jetir.org/papers/JETIR2305294.pdf