Determining water stress in plants and how this affects their growth and yield quality is not an easy task. This is usually done by monitoring the soil’s water stress and by manual measurements or visual inspection to detect any premature sign of wilting. However, these methods are unreliable and still require human intervention on the field. The proposed instrumented field is a step stone towards automated crop growth monitoring by means of low-cost equipment. The tested crops are string beans and tomatoes. The experiment was aimed at determining their water stress according to specific water conditions set beforehand on each field’s row. Data acquired during the testing period include soil temperature and moisture, air temperature and humidity, and daily color images taken every 15 min. Preliminary analysis conducted on images by means of traditional image processing techniques allowed analysis of fruits and leaves health and water status.

How water affects growing plants: an experimental set-up based on 2D vision and on-the-edge sensing

Pasinetti, Simone
Conceptualization
;
Nuzzi, Cristina
Validation
;
Botturi, Davide
Data Curation
;
Coffetti, Gabriele
Resources
;
Mangili, Sara
Methodology
;
Collina, Manuel
Methodology
2022-01-01

Abstract

Determining water stress in plants and how this affects their growth and yield quality is not an easy task. This is usually done by monitoring the soil’s water stress and by manual measurements or visual inspection to detect any premature sign of wilting. However, these methods are unreliable and still require human intervention on the field. The proposed instrumented field is a step stone towards automated crop growth monitoring by means of low-cost equipment. The tested crops are string beans and tomatoes. The experiment was aimed at determining their water stress according to specific water conditions set beforehand on each field’s row. Data acquired during the testing period include soil temperature and moisture, air temperature and humidity, and daily color images taken every 15 min. Preliminary analysis conducted on images by means of traditional image processing techniques allowed analysis of fruits and leaves health and water status.
2022
978-1-6654-6998-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/567024
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