A Comparative Study of Conventional and Smart Farming

Agriculture is at the heart of all occupations in developing countries, and with developing technologies, the application should be cost-effective and efficient. The proposed setup includes low-cost moisture, temperature sensors for optimizing water usage and yield, and radar sensors for monitoring any invasion in the farm. The setup is aimed to provide a study a miniature setup representing smart agriculture including smart water management with consistent monitoring for weather conditions in the present and future. An intelligent invasion monitoring system which can indicate animals or specifically pests invading the fields. This setup represents a part of a grid which will be utilizing solar power to prevent periodic replacements of batteries, and for this purpose, a solar panel will be used in the miniature farm. The main objective is to provide a comparative study of smart farms to conventional farms; these smart farms employ machine learning algorithms in real time to tackle problems related to water and energy. The Internet of things and machine learning have been advancing industrial purposes in each and every way, and finding its way in agriculture is still difficult due to the expenses which might not be affordable for a farmer. This research is a step toward efficient yet cost-effective farming.