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Putting deep learning in perspective for pest management scientists.

Robert D Clark¹

¹Simulations Plus, Inc., Lancaster, CA, USA.

Pest Management Science

|March 17, 2020

Summary

This summary is machine-generated.

Deep learning (DL) offers transformative potential but requires careful application. Its primary impact in agrochemicals may be automating efficacy assessments, necessitating robust data and expertise.

Keywords:

CNN DNN QSAR RNN artificial intelligence convolutional neural networks deep learning deep neural networks quantitative structure-activity relationship recurrent neural networks

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Area of Science:

Artificial Intelligence
Machine Learning
Agrochemical Development

Background:

Deep learning (DL) is rapidly changing scientific fields, sparking debate about its societal impact.
Over-reliance on DL as a 'black box' tool can overlook limitations, especially with imperfect data.
Understanding DL's historical context and relation to older AI forms is crucial.

Purpose of the Study:

To provide a mechanistic and historical context for deep learning.
To explain the operational principles of deep learning.
To explore challenges associated with deep learning implementation in pest management.

Main Methods:

Relating deep learning to older artificial intelligence techniques.
Explaining the general operation of deep learning algorithms.
Illustrating deep learning applications and challenges using pest management examples.

Main Results:

Deep learning's near-term impact on agrochemical development is likely in automating efficacy assessments.
Successful implementation requires significant investment in large, curated datasets and expert evaluation.
Deep learning is expected to complement, rather than replace, existing machine learning in pesticide discovery.

Conclusions:

Deep learning presents opportunities and challenges in scientific applications, particularly in agrochemical development.
Addressing data quality and developing expertise are critical for realizing DL's potential.
Deep learning will likely augment current machine learning methodologies in pesticide research.