Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree

Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree

This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture. The framework employs a hierarchical decision-making structure with conditional action masking, where high-level actions guide the robot's...

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Bibliographic Details
Main Authors: Mahsa Khosravi, Zhanhong Jiang, Joshua R. Waite, Sarah E. Jones, Hernan Torres Pacin, Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Smart Agricultural Technology
Subjects:
Deep reinforcement learning
Conditional action tree
Navigation
Chemical spraying
Precision agriculture
Yield recovery
Online Access:http://www.sciencedirect.com/science/article/pii/S2772375525004253
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