000			02535nab|a22003617a|4500
001			68811
003			MX-TxCIM
005			20250526100656.0
008			2505142022|||||-uk||p|op||||00||0|eng|dd
022			_a0377-2063
022			_a0974-780X (Online)
024	8		_ahttps://doi.org/10.1080/03772063.2022.2060868
040			_aMX-TxCIM
041			_aeng
100	1		_aDaniel, J. _938819
245	1	0	_aRANC-CROP recommendation attributed to soil nutrients and stock analysis using machine learning
260			_aUnited Kingdom : _bTaylor and Francis Ltd., _c2023.
500			_aPeer review
520			_aAgriculture 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.
546			_aText in English
650		7	_aNeural Networks _2AGROVOC _99056
650		7	_aCapital markets _2AGROVOC _938820
650		7	_aPrices _2AGROVOC _95255
650		7	_aCrops _2AGROVOC _91069
650		7	_aOrganic fertilizers _2AGROVOc _91740
650		7	_aSoil fertility _2AGROVOC _91952
650		7	_aMachine learning _2AGROVOC _911127
700	1		_aShyamala, R. _938821
700	1		_aPugalenthi, R. _938822
700	0		_aMohan Kumar _938823
773	0		_tIETE Journal of Research _gv. 69, no. 11, p. 8077-8089 _dUnited Kingdom : Taylor and Francis Ltd., 2023. _x0377-2063
942			_cJA _n0 _2ddc
999			_c68811 _d68803