Agriculture and its related sectors are by far the most important sources of income in pastoral India. The agriculture sector contributes significantly to the country's Gross Domestic Product (GDP). Still, the yield per hectare of crops is tragically low in compared to international standards. This is one among the key reasons for the high rate of self-murder among India's boundary cultivators. For growers, this study presents a realistic and stoner-friendly yield vaticination technique. Growers are connected to the stoner system via a mobile operation. Growers are connected to the stoner system via a mobile operation. As input, the stoner offers the area and soil type. Growers can use machine literacy algorithms to select the best profitable crop list or predict crop yield for a stoner named crop. Crop productivity is predicted using machine learning methods such as the , Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF). With a delicacy of 95%, the Random Forest delivered the most exquisite outcomes. Furthermore, the system suggests the optimal moment to apply treatments to boost production.
Software And Hardware