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Monitoring the forest‟s weather has been essential to living things over the years. Currently, there is a shortage of information on real-time temporal and spatial environmental conditions of the forest that drive forest health condition. This work focuses on the sensing of humidity and temperature as weather data from the forest. Unlike the traditional systems used to collect weather information, the use of wireless sensor network (WSN) gives real-time data capture from every point of the forest. However, the WSN faces the number of challenges including low bandwidth, low power, and short battery lifespan. Reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, this work proposes an Energy Efficient Scheduling using Deep Reinforcement Learning (DRL) ( S-DRL) algorithm in WSN. S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: Clustering phase, Duty-Cycling phase and Routing phase. S-DRL starts with the clustering phase where it reduces the energy consumption incurred during data aggregation. It is achieved through Zone based Clustering (ZbC) scheme. In ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing DRL algorithm. Here, the sensor node autonomously decides its sleep or wakeup time to transmit sensed data to the head node. From which, S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in third (routing) phase using Ant …