Development, optimization and modelling of performance parameters for remote-controlled mechatronic precision planter using RSM and Hybrid PSO-ANN model

Abstract

• Developed a remote-controlled mechatronic precision planter tailored for cotton, ensuring accurate seed placement with uniform spacing and optimal depth. • Employed a Hybrid PSO-ANN model to enhance operational efficiency and optimize seed metering performance. • Investigated the effects of inclination angle, seed hole geometry, and forward speed on seed spacing and planter efficiency. • Conducted laboratory experiments to optimize planting parameters using Response Surface Methodology (RSM) and hybrid ANN PSO optimization techniques. • Achieved precision planting aligned with recommended spacing (45 cm), minimizing misses and overlaps, and improving crop yield. Precision planting plays a crucial role in optimizing crop production by ensuring accurate seed placement at the correct depth, uniform spacing, and adequate soil coverage. Inclined plate metering systems are particularly favored for their precision and cost-effectiveness, especially in scenarios involving multiple crops. Researchers are exploring advanced methods such as ANN and PSO to optimize seed metering. Mechatronics-based systems show promise by integrating IoT and robotics to enhance precision and efficiency in planting operations. A recent study focused on designing a seed metering system tailored for cotton (Gossypium hirsutum L) based on prevalent seed characteristics in the Saurashtra region of Gujarat, India. The study involved designing a planter equipped with a ground wheel, optical rotary encoder, microprocessor, stepper motor, and seed metering box. Experimental evaluations were conducted to optimize seed sowing uniformity and operational parameters using laboratory tests and hybrid techniques combining ANN with PSO algorithms. This approach effectively optimized the precision and operational efficiency of the mechatronic precision planter for cotton crop planting. The investigation further delved into the impact of inclination angle (A), seed hole geometry (P), and forward speed (S) on various cotton seed planting parameters using a mechatronic precision planter. Results indicated that with a maximum deviation of just 2.67 percent between the experimental and model-predicted average seed spacing values, the ANN-PSO model accurately approximates the ideal input parameter values for maximizing average seed spacing. The forward speed (S), inclination angle (A), and geometry of the seed hole (P) to maximize average seed spacing are found to be 0.43 m/s, 52 degrees, and 100 percent, respectively. Under the optimized conditions, the predicted values for average seed spacing, miss index, multiple index, quality of feed index, and cell fill percentage were 44.92 cm, 2.72%, 7.05%, 90.03%, and 104.67%, respectively. The integration of PSO with prediction models offered insights into enhancing precision planting efficiency and overall crop yield in mechatronic precision planters.