Enhancing agricultural productivity through a semi-autonomous IOT robot in smart farming systems

Abstract

In addressing the challenge of enhancing agricultural productivity in developing countries, this research introduces a semi-autonomous IoT robot designed to modernize traditional farming practices in regions like Bangladesh. The study explores whether such a robot can effectively integrate with existing farming practices and assesses its impact on agricultural productivity, resource optimization, and most importantly cost-efficiency. The literature reveals a push towards smart farming technologies, but their adoption in less affluent regions is hindered by cost and resource constraints. Employing a mixed-methods case study approach, the research developed and tested a robot equipped with an NPK sensor for detecting levels of nitrogen, phosphorus, and potassium in the soil, a water level indicator to measure flood water levels in millimeters, and a soil moisture sensor. These data were transmitted to the user's phone over the internet, allowing for remote monitoring of fertilizers and water levels. Additionally, the system included a remote-controllable water dispenser for irrigation and a fruit-picking mechanism for harvesting. The results indicated that all intended data collection was executed accurately, enabling users to remotely monitor soil conditions and effectively control the robot's actions. However, the initial cost of the robot may be slightly expensive for individual farmers, though mass production is anticipated to reduce the price to a level that is reasonably affordable for widespread adoption. However, limitations in sensor calibration for different soil types are acknowledged. Future research suggested exploring sensor calibration precision, extending system capabilities, and integrating predictive AI for a comprehensive agricultural solution. Bangladesh J. Agri. 2023, 48(2): 95-105