• PRANEETHA SARABU
• THOTA.AASRITHA
• SAMANTAPUDI SAIKIRAN
• KONDETI PRASANTH
• C.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. Because traditional measurements are time-consuming and expensive, reflectance-based estimates of soil properties such as texture, organic matter content, water content, and nutrient status are attractive. Soil properties have been related to reflectance measured with laboratory, in-field, airborne, and satellite sensors. By using the machine learning algorithms here, we can predict the crop. We are using two different algorithms. SVM (SUPPORT VECTOR MACHINE), RF (RANDOM FOREST).

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

"SOIL QUALITY ASSESSMENT BY USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.b270-b274, May-2023, Available :http://www.jetir.org/papers/JETIR2305134.pdf

"SOIL QUALITY ASSESSMENT BY USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppb270-b274, May-2023, Available at : http://www.jetir.org/papers/JETIR2305134.pdf