Abstract
Agriculture Productivity is a numerical representation, whereas agriculture efficiency is a qualitative assessment. Further efficiency can be used in a variety of agro-climatic situations and crops, as efficiency refers to the most optimization of resources. On the other hand, in reduction of energy consumption, energy efficiency is an important aspect of sustainable energy conservation. As a result, increasing agricultural energy efficiency is critical for lowering energy demand and, as a result, prices. Improvements in agricultural energy efficiency are defined as a reduction in primary energy consumption for the manufacturing of a unit of agricultural commodity within farm bounds. Energy allocation of agricultural production expenses varies greatly by activity, production practice, and location, and growing energy import dependency for lubricants and nutrients has raised worries about the impact on agriculture. In order to achieve increased agricultural productivity, resource and energy allocation in production planning is critical. The integration of multiple data sources to create reliable, precise, and valuable information is a challenging task in agricultural resource management. In order to overcome the resource allocation problem and enhance efficiency, While collecting the data for computing in terms of processing agriculture resources such as temperature data, soil data, crop growth data, humidity data, and water level data, the traditional data fusion algorithms lack computational complexities. That results in the attainment of poor energy efficiency. To overcome the above problem, our proposed algorithm, called the naive multi-phase resource allocation algorithm, guarantees the effective utilization of agricultural resources in a dynamic agriculture environment that ensures energy efficiency.