Daily Archives: January 22, 2025

Abstract-This research focuses on the design and development of an innovative seed sowing machine aimed at improving agricultural productivity through precision and efficiency. Traditional sowing methods, which are either manual or use basic machinery, face challenges like inconsistent seed spacing, high labor requirements, and frequent blockages in seed dispensing tubes. These issues lead to reduced crop yields, increased operational costs, and significant seed wastage.The proposed seed sowing machine addresses these limitations by integrating automated seed dispensing, consistent depth control, and a blockage detection system using sensors. This machine is designed to place seeds uniformly at a specific depth and spacing, enhancing germination rates and ensuring even crop growth. Testing results demonstrate improved accuracy in seed placement and reduced downtime, showing a potential to save up to 40% of labor compared to traditional methods.Overall, this seed sowing machine offers a cost-effective and efficient solution for small to medium-scale farmers, enabling more sustainable and productive farming. This research lays the groundwork for future advancements in automated agricultural machinery, contributing to the broader goal of technological innovation in agriculture.

Abstract-This paper discusses the identification of faults in a Francis turbine using vibration-based condition monitoring with Minitab and MATLAB. The vibration signals are analyzed to detect faults in the motor components of the turbine. Statistical analysis uses Minitab to identify trends, while MATLAB carries out advanced signal processing, including Fast Fourier Transform (FFT) and wavelet analysis, to extract fault features. The combined approach effectively diagnoses issues like misalignment and bearing defects, showing its value in predictive maintenance for improved turbine performance.