WEB BASED AGRICULTURE ASSISTANCE USING MACHINE LEARNING
1* R. UTTHAM SAI, 2*R. SHASHI KUMAR, 3*S. MURALI KRISHNA,
4*S. VAMSHI KRISHNA, 5*S. NITHIN KUMAR
1 Assistant Professor, 2,3,4,5 B.Tech FinaL Year
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
NALLA MALLA REDDY ENGINEERING COLLEGE
DIVYANAGAR, HYDERABAD, INDIA.
Abstract –- Crops are grown on fields, which are depending on the quality of the soil, in agriculture, a crucial industry. In order to assist agriculture, we have developed Agriculture Assistance, a project that investigates how machine learning might help precision farming at scale using validated and trustworthy data. Crop recommendation, fertilizer recommendation, and disease prediction are the three applications that our website combines. Users may determine which crop would be most suitable to cultivate in a specific location using our Crop Recommendation tool. Users of the Fertilizer Recommendation application can enter information about the crop type and the soil to determine whether there are nutrient surpluses or shortages and obtain suggestions for improving the soil. In the Disease Prediction application, we also leverage OpenCV image recognition technology to forecast crop diseases by looking at photos. With the aid of these machine learning tools, farmers may be able to make better educated decisions that result in more effective resource utilization and maybe higher agricultural yields. We are able to offer insightful information on crop choice, planting times, irrigation, and nutrient management by analyzing data from numerous sources, such as soil samples and weather forecasts. When crop diseases are promptly diagnosed, farmers may take preventative action, reducing losses and enhancing food security. The ultimate goal of agriculture assistance is to increase agricultural output and promote economic expansion in this vital industry.
Keywords– Machine Learning, Random Forest, Decision Tree, OpenCV, Crop Recommendation, Fertilizer Recommendation, Disease Detection.
