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.