<ul><li>Precision agriculture requires efficient autonomous systems for crop monitoring in large-scale environments while minimizing resource consumption.</li><li>A two-stage deep learning framework is proposed, utilizing a pre-trained LSTM as a belief model to update a probabilistic map of the environment and prioritize informative regions for exploration.</li><li>Three agent architectures were compared: untrained IG-based agent, DQN agent with CNNs and belief, entropy, and POV mask, and Double-CNN DQN agent with wider spatial context.</li><li>Results demonstrate that uncertainty-aware policies leveraging entropy, belief states, and visibility tracking lead to robust and scalable exploration in agricultural fields, with future work focusing on enhancing learning efficiency and policy generalization.</li></ul>

Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration

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