Random Forest (RF), Support Vector Machine (SVM), Naive Bayes, ensemble machine learning, Gross domestic product (GDP)
The Indian economy is dominated by the agricultural sector. Agriculture is one the largest employment sectors in India. Most of the population of India is employed in the agricultural sector. Although India is an agriculture-based country, the Gross domestic product (GDP) of this sector is not enough. Only 16 percent is contributed to the agricultural sector. Due to industrialization, excessive use of pesticides badly affects soil productiveness. Most of the methods implemented by farmers using their experiences are not enough to increase productivity as needed. A common problem among Indian farmers is that they do not choose the best quality products due to non adequate knowledge & financial constraints. The goal of production is to maximize the yield and minimize the farming cost. The crop recommendation process is to provide farmers with accurate advice based on their geographical region and demands. It uses a variety of input data such as soil characteristics considering parameters of N, K, P, pH, rainfall and temperature to recommend the appropriate crop. Traditional crop recommendation systems [1] do not take all the parameters that may affect product performance. The system integrates inputs that make predictions of crops in advance & the farmers to choose products for avoiding loss. The planning process can also help farmers select crops for rotation. Algorithms such as machine learning i.e. Random Forest (RF), Support Vector Machine (SVM), Naive Bayes, ensemble machine learning can analyze large amounts of datasets to make more accurate decisions. Using advanced techniques it increases crop yields[2], improves resource management & greater profitability for farmers.
Article Details
Unique Paper ID: 159538

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 296 - 300
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