Bud Yield Detection and Quality

Authors- Padma Priyanka, Vedant Kumbhare, Zuveb Kamdoli, Pranay Wagh, Dr. Arvind Jagtap

Abstract-The abstract presents a concise overview of the study on bud yield detection, highlighting its objectives, methodology, findings, and potential implications. Bud yield detection is crucial in optimizing agricultural practices and ensuring efficient crop management. This study aims to develop a reliable and efficient method for accurately assessing bud yield in various plant species. The methodology involves the utilization of advanced technologies such as computer vision and machine learning algorithms. High-resolution images of plants are captured at different growth stages, focusing on bud development. These images are then processed to extract relevant features and characteristics of buds. Machine learning models are trained using these features to predict bud yield based on the observed patterns and relationships. The findings of this study reveal a strong correlation between the extracted features and the actual bud yield of the plants. The developed machine learning models demonstrate a high degree of accuracy in predicting bud yield, providing a valuable tool for farmers and researchers to assess and manage crop production. By accurately estimating bud yield early in the growth cycle, farmers can make informed decisions about resource allocation, irrigation, fertilization, and harvesting schedules. The implications of this research are significant for sustainable agriculture and food security. The ability to predict bud yield with precision contributes to reducing wastage, optimizing resource usage, and increasing overall crop productivity. Additionally, this study paves the way for the integration of technology-driven approaches into traditional farming practices, bridging the gap between modern innovation and age-old cultivation methods.

DOI: /10.61463/ijset.vol.12.issue2.130