NEXT GENERATION CROP MANAGEMENT AND OPTIMIZATION PLATFROM

Abstract

The rapid growth of population and increasing
demand for food production have created major
challenges for the agricultural sector. Traditional
farming methods are no longer sufficient to achieve
higher productivity because agriculture depends on
several environmental and soil-related factors such
as temperature, humidity, rainfall, soil pH, and
nutrient levels including nitrogen, phosphorus, and
potassium. Farmers often face difficulties in
selecting suitable crops for specific climatic and
soil conditions, which leads to low yield and
financial losses. To overcome these challenges, the
proposed system “Next Generation Crop
Management and Optimization Platform”
introduces a smart agriculture solution using
Machine Learning and Data Analytics techniques.
The system is designed as a web-based platform
that analyzes agricultural data and predicts the most
suitable crop for cultivation based on soil and
weather parameters. It also estimates crop yield
using historical agricultural data and predictive
algorithms. The proposed model uses machine
learning techniques such as Decision Tree, Random
Forest, K-Nearest Neighbor, Logistic Regression,
and Stacking Classifier to improve prediction
accuracy and provide reliable recommendations to
farmers. The system supports efficient utilization of
resources like water, fertilizers, and land by helping
farmers make informed decisions before
cultivation. Additionally, the platform can be
integrated with IoT sensors, weather forecasting
systems, and satellite data to enhance real-time
monitoring and prediction accuracy. By adopting
such smart agricultural technologies, farmers can
reduce crop failure risks, improve productivity,
increase profitability, and support sustainable
farming practices. This system contributes to the
modernization of agriculture and helps bridge the
gap between traditional farming and digital
agricultural solutions.