Paper Key : IRJ************470
Author: Prathamesh Penshanwar,Devendra Bawanthade,Gaurav Mali,Pragati Kokate,P. R. Bhakare
Date Published: 05 Apr 2024
Abstract
The use of machine learning techniques to improve agricultural research by analyzing intricate connections in cropping systems is covered in the abstract for the Agricultural Sowing Machine and Monitoring System. Significant yield disparities based on parameters such as sowing date can be revealed by the system through analysis of site-specific crop responses and management interactions. By using large databases and artificial intelligence algorithms, this strategy seeks to address future food demands, discover sustainable practices, and expedite agricultural research. There is significant potential for raising agricultural yields due to the system's capacity to examine interactions between infinite cropping systems, which are difficult to assess using conventional techniques. All things considered, the Agricultural Sowing Machine and Monitoring System offers a viable way to increase agricultural output and satisfy the needs of an expanding world population while addressing the effects of climate change.
DOI Requested