Optimising Energy Efficiency in Agricultural Consumer Electronics Using Principal Component Analysis and Deep Q-Learning

The ability to reduce emissions and improve sustainability in agricultural consumer electronics has been significantly hindered due to the use of energy-intensive technology within the agricultural sector. This study proposes a new enhancement of deep Q-learning (DQN) with principal component analysis (PCA) focused on energy efficiency. PCA helps manage massive operational data by performing dimensionality reduction, whereas DQN, a reinforcement learning paradigm, optimises decision-making during real-world interactions. The main contribution of this study is in the combined use of PCA and DQN to form customisable, precise, contest-responsive energy frameworks powered by real-time analytics on agricultural data—energy management on such a scale has not been approached in the context of sustainable agriculture before. The experiments confirm the optimal model, further achieving a cumulative reward of 72.56, an average emission of 1.83, a Q-value of 24.76 and a total zenith value of 75.40% in ensuring numerous noncriteria-defined efficient energy-dependent operations. This paradigm not only fills the void in the automation of passive intelligent agricultural systems but also serves as a point of reference for other eco-critical domains to strive towards greener technology.