On-farm experimentation and machine learning in the development of intelligent decision systems in agriculture: a synthesis of the Australian Future Farm project

A.F. Colaço; R.G.V. Bramley; B. Whelan

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On-farm experimentation and machine learning in the development of intelligent decision systems in agriculture: a synthesis of the Australian Future Farm project

In: Precision agriculture '25

Authors:

A.F. Colaço

A.F. Colaço
andre.colaco@usp.br

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R.G.V. Bramley

R.G.V. Bramley

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B. Whelan

B. Whelan

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Type:: Chapter

Pages:: 1217–1224

DOI:: https://doi.org/10.1163/9789004725232_159

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Dar Hadith al Hassania

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In the context of fertilizer management, machine learning (ML) has been restricted to predicting crop and soil parameters used as input to generalized recommendation frameworks. Consequently, while input information has improved, fertilizer recommendations still rely on simplistic agronomic rules. This work presents outcomes from the ‘Future Farm’ project, established to re-examine and improve the way in which digital technologies are used to inform decisions about input management, focusing on nitrogen fertilization. Results indicate that effective data-driven decisions combine ML with site-specific crop response information from on-farm experimentation, avoiding the generalized agronomic rules inherent to current recommendation methods.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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On-farm experimentation and machine learning in the development of intelligent decision systems in agriculture: a synthesis of the Australian Future Farm project

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Precision agriculture '25

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