JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
A Comparative Analysis of Machine Learning predictions for crop yield
Manasa P R, Arun Kumar K L
Since most Indians are employed in agriculture, it is widely known that India is the world's liveliest nation. so we say agricultures the backbone of India, usually Farmers cultivate same crops over the years without trying other types of crops Farmers often apply fertilizers in random quantities, sometimes without knowing the specific deficiencies and required amounts for the soil. This practice can negatively impact crop yield and lead to soil acidification and damage to the top soil layer. Therefore, in order to solve this issue and improve the lives of farmers, we are developing a system with machine learning algorithms. Based on that content and with the aid of additional elements like weather, humidity, and temperature, our algorithm will recommend which crop should be planted on a specific plot of land. The system will provide information about the quantity of fertilizers how much should be put in order to grow a particular crop for a suitable land or seed for planting. Therefore, by using our technology, farmers can grow a new and unusual variety of crop, thereby increasing their profit margin while avoiding soil pollution. Based on real data an advice model predicts crop production with 92 percent of accuracy.
Mathematical models, Random Forest, logistic regression models, k-nearest neighbor, crop recommendations and CYP.