# Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree

arXiv: https://arxiv.org/abs/2503.17985 · HTML: https://mlsanity.com/paper/2503.17985/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 11 of 11)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Reinforcement Learning with Parameterized Actions](https://mlsanity.com/paper/1509.01644/) (2015)
- [Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1903.01344/) (2019)
- [Is the Policy Gradient a Gradient?](https://mlsanity.com/paper/1906.07073/) (2019)
- [PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping](https://mlsanity.com/paper/2312.12065/) (2023)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Generalising Discrete Action Spaces with Conditional Action Trees](https://mlsanity.com/paper/2104.07294/) (2021)
- [AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning](https://mlsanity.com/paper/2409.00735/) (2024)

## Built on by (most cited first, 2 of 2)

- [Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learning](https://mlsanity.com/paper/2510.24650/) (2025)
- [AgriAgent: Contract-Driven Planning and Capability-Aware Tool Orchestration in Real-World Agriculture](https://mlsanity.com/paper/2601.08308/) (2026)

## Abstract (arXiv)

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 direct the robot's exploration, while low-level actions optimize its navigation and efficient chemical spraying in affected areas. The key objectives of optimization include improving the coverage of infected areas with limited battery power and reducing chemical usage, thus preventing unnecessary spraying of healthy areas of the field. Our numerical experimental results demonstrate that the proposed method, Hierarchical Action Masking Proximal Policy Optimization (HAM-PPO), significantly outperforms baseline practices, such as LawnMower navigation + indiscriminate spraying (Carpet Spray), in terms of yield recovery and resource efficiency. HAM-PPO consistently achieves higher yield recovery percentages and lower chemical costs across a range of infection scenarios. The framework also exhibits robustness to observation noise and generalizability under diverse environmental conditions, adapting to varying infection ranges and spatial distribution patterns.