Small data deep learning methodology for in-field disease detection: Case study of potato crops

D. Herrera-Poyatos; J. Domínguez-Rull; R. Montes; I. Hernández; I. Barrio; J. Tardaguila; F. Herrera; A. Herrera-Poyatos

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Small data deep learning methodology for in-field disease detection: Case study of potato crops

In: Precision agriculture '25

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D. Herrera-Poyatos

D. Herrera-Poyatos null

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J. Domínguez-Rull

J. Domínguez-Rull null

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R. Montes

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I. Hernández

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I. Barrio

I. Barrio null

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J. Tardaguila

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F. Herrera

F. Herrera null

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A. Herrera-Poyatos

A. Herrera-Poyatos null

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Type:: Chapter

Pages:: 356–362

DOI:: https://doi.org/10.1163/9789004725232_045

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Dar Hadith al Hassania

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Early detection of diseases in crops is essential to prevent harvest losses and improve the quality of the final product. In this context, the combination of machine learning and proximity sensors is emerging as a technique capable of achieving this detection efficiently and effectively. This approach has been applied to potato crops to detect late blight (Phytophthora infestans). However, most of these AI models found in the specialised literature have been developed using leaf-by-leaf images taken in the lab, which does not represent field conditions and limits their applicability. This study presents the first machine learning model capable of detecting mild symptoms of late blight in potato crops through the analysis of high-resolution RGB images captured directly in the field, overcoming the limitations of other publications in the literature and presenting real-world applicability.

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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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Small data deep learning methodology for in-field disease detection: Case study of potato crops

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Precision agriculture '25

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