Learning-based crop management optimization using multi-stream convolutional neural networks

Ormiga Galvao Barbosa, Alexandre

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https://hdl.handle.net/2142/108438 Copy

Description

Title

Learning-based crop management optimization using multi-stream convolutional neural networks

Author(s)

Ormiga Galvao Barbosa, Alexandre

Issue Date

2020-07-06

Director of Research (if dissertation) or Advisor (if thesis)

• Hovakimyan, Naira
• Martin, Nicolas

Doctoral Committee Chair(s)

Hovakimyan, Naira

Committee Member(s)

• Salapaka, Srinivasa
• Voulgaris, Petros
• Shi, Humphrey

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Convolutional Neural Networks
• Deep Learning
• On-farm Research
• Yield Modeling
• Risk-averse Optimization
• Deep Ensemble
• Reinforcement Learning
• Coverage Path Planning
• Imitation Learning

Abstract

Improving crop management is an essential step towards solving the food security challenge. Despite the advances in precision agriculture, new methods are needed to create decision-support systems to help farmers increase productivity while accounting for environmental impacts and financial risks. This dissertation presents a class of learning-based optimization algorithms for spatial allocation of crop inputs, and a new framework for online coverage path planning with potential use in tasks such as planting and harvesting. The proposed algorithms use Multi-stream Convolutional Neural Networks (MSCNN) to learn relevant spatial features from the environment and use them to optimize the available control inputs. In the crop inputs optimization problem, an MSCNN combines five input variables as in a regression problem to better predict yield. The predictive model is then used as the base of a gradient-ascent algorithm to maximize a custom objective function. To leverage the applicability of this algorithm, a risk-aware version of this method is also proposed. The predictive uncertainty is measured and used as a constraint to comply with different levels of risk-aversion. Experiments with real crop fields demonstrate that this method significantly reduces the yield prediction errors when compared to the state of the art algorithms. Results from the optimization algorithm show an increase in the expected net revenue of up to 6.8% when compared with the status quo management while providing safety bounds. In the coverage path planning framework, an MSCNN agent learns a control policy from demonstrations of paths obtained offline through heuristic algorithms, by using imitation learning. The resulting control policy is further improved through policy-gradient reinforcement learning. Simulations show that the improved control policy outperforms the offline algorithms used during the imitation learning phase, and that the proposed framework can be easily adapted to different cost functions.

Graduation Semester

2020-08

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/108438

Copyright and License Information

Copyright 2020 Alexandre Ormiga Galvao Barbosa

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