Optimizing Crop Yield and Profitability with an FPGA Enabled Smart Irrigation System for Agriculture

This research enhances agricultural productivity and crop quality using advanced embedded technology in multi-crop farming systems. The proposed system incrementally develops logic, connects sensor nodes, and operates through iterative feedback loops by leveraging embedded processing techniques and irrigation engineering. Key to its efficiency are “filaments” (real-time input/output devices) and FPGA (Field-Programmable Gate Array) technology, enabling dynamic partial self-reconfiguration. Each crop undergoes independent trials to establish optimal strategies for maximizing yield quantity and quality. Fuzzy logic handles the uncertainties of agricultural processes, while the Random Forest algorithm predicts soil moisture levels to determine precise irrigation timing and quantity across different field sections. An autonomous irrigation system, driven by FPGA, employs solenoid valves to regulate water flow. Additionally, a multi-data processing platform monitors critical environmental factors, including soil moisture, temperature, humidity, and water levels, aiming to optimize crop output. The advantages include increased crop yield per unit area and reduced operational costs, with a prototype enabling centralized management, statistical analysis, and real-time monitoring, promoting economic growth in rural areas. Future plans involve scaling the system through farmer education and rural networks, with potential for district and state-wide implementation. This research addresses modern agriculture challenges and improves resource efficiency, fostering economic development through innovative technology.