TY  - JA
AU  - Daniel,J.
AU  - Shyamala,R.
AU  - Pugalenthi,R.
AU  - Mohan Kumar
TI  - RANC-CROP recommendation attributed to soil nutrients and stock analysis using machine learning
SN  - 0377-2063
PY  - 2023///
CY  - United Kingdom
PB  - Taylor and Francis Ltd.
KW  - Neural Networks
KW  - AGROVOC
KW  - Capital markets
KW  - Prices
KW  - Crops
KW  - Organic fertilizers
KW  - AGROVOc
KW  - Soil fertility
KW  - Machine learning
N1  - Peer review
N2  - Agriculture is India's greatest wealth, which also contributes to the country's economic development and defines the standard of living for more than 50 percent of the Indian population. In addition to this conventional crop, more are grown and have high dependency, such as wheat and rice. Farmers face many problems where sustainability is of primary importance in agriculture. To solve the issue, we propose to develop a “RANC (Recommendation Analysis by Soil Nutrients of Crops) Crop Recommendation Tool” web application that will help farmers generate their high income with effective crop cultivation along with the suggestion of organic fertilizer by providing up-to - date stock information, Soil Test Report, crop yield time and nutritional value of each crop. The RANC algorithm is used to pick crops and the Deep Neural Network is used for price prediction to improve the farmer’s choice of crops for cultivation with a high benefit. In the case of crop selection, an existing model uses the Soil Test Report to generate the quantity of fertilizers needed to expand. We use SVM for linear data regressions and ANN, RNN, RBM for non-linear data in the case of price estimation in stock analysis. From the experimental results, the prediction accuracy over 90% has been achieved for the proposed approach
T2  - IETE Journal of Research
DO  - https://doi.org/10.1080/03772063.2022.2060868
ER  -