Contents I. Introduction
Agriculture is the foundation of human existence and serves as the primary source of food and raw materials. Its role in economic growth is pivotal, generating substantial employment opportunities. However, conventional farming methods often face issues such as a shortage of skilled labor, reduced crop productivity, and unavailability of time-efficient technologies. Furthermore, the global population is rapidly increasing, and the demand for food is growing exponentially. However, farmers still need to rely on traditional farming, which is time-consuming, labor-intensive, and susceptible to human errors with low yields. So, our goal is to design and develop significant advancements in technology to transform traditional farming methods into highly efficient and productive systems. One such improvement was a five-year study conducted to find how excessive use of ammonium-based nitrogen fertilizer caused soil acidification, which affected the yield of lentils, peas, and winter wheat by R. L. Mahler. et al [1]. Another work was an innovative automated irrigation system developed to enhance water efficiency in agriculture by J. Gutiérrez et al. This system featured wireless sensors distributed in the plant root zones, monitoring soil moisture and temperature. A central gateway unit managed sensor data, activated actuators, and communicated with a web application [7]. M. Ayaz et al. proposed various IoT-based architectures, platforms, and sensors with many agricultural applications, including soil monitoring, crop health assessment, and yield optimization. They also discussed the potential for IoT to improve food quality, safety, and transportation [15]. In this context, a transformative solution to address the challenges faced by traditional agriculture is the integration of RF-controlled robots in cultivation processes. This robot will collect soil samples with its soil digger module and measure the moisture level of the collected sample with the help of a sensor. pH level sensing module will also measure the pH value through a critical analysis. Fertilizer/pesticide spraying will be conducted through the help of a sprayer module. Data will be monitored with precision and efficiency with the help of advanced data analytics through the Farmbot mobile application. Utilizing intricate computational process, the Farmbot mobile application will analyze sensor data and enable itself to provide appropriate suggestions with optimal efficiency. That’s how a cost-effective and efficient solution for the farmers is led to by an Arduino-based robot named "Farmbot" presented in this project.
