678 research outputs found
New strategies for row-crop management based on cost-effective remote sensors
Agricultural technology can be an excellent antidote to resource scarcity. Its growth has
led to the extensive study of spatial and temporal in-field variability. The challenge of
accurate management has been addressed in recent years through the use of accurate
high-cost measurement instruments by researchers. However, low rates of technological
adoption by farmers motivate the development of alternative technologies based on
affordable sensors, in order to improve the sustainability of agricultural biosystems.
This doctoral thesis has as main objective the development and evaluation of systems
based on affordable sensors, in order to address two of the main aspects affecting the
producers: the need of an accurate plant water status characterization to perform a
proper irrigation management and the precise weed control.
To address the first objective, two data acquisition methodologies based on aerial
platforms have been developed, seeking to compare the use of infrared thermometry
and thermal imaging to determine the water status of two most relevant row-crops in the
region, sugar beet and super high-density olive orchards. From the data obtained, the
use of an airborne low-cost infrared sensor to determine the canopy temperature has
been validated. Also the reliability of sugar beet canopy temperature as an indicator its
of water status has been confirmed. The empirical development of the Crop Water Stress
Index (CWSI) has also been carried out from aerial thermal imaging combined with
infrared temperature sensors and ground measurements of factors such as water
potential or stomatal conductance, validating its usefulness as an indicator of water
status in super high-density olive orchards.
To contribute to the development of precise weed control systems, a system for detecting
tomato plants and measuring the space between them has been developed, aiming to
perform intra-row treatments in a localized and precise way. To this end, low cost optical
sensors have been used and compared with a commercial LiDAR laser scanner. Correct
detection results close to 95% show that the implementation of these sensors can lead
to promising advances in the automation of weed control.
The micro-level field data collected from the evaluated affordable sensors can help
farmers to target operations precisely before plant stress sets in or weeds infestation
occurs, paving the path to increase the adoption of Precision Agriculture techniques
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Sin resume
A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in
the last few decades as the direct and indirect methods available are laborious and
time-consuming. The recent emergence of high-throughput plant phenotyping platforms has
increased the need to develop new phenotyping tools for better decision-making by breeders. In
this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue
(RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The
model mixes numerical data collected in a wheat breeding field and visual features extracted from
the images to make rapid and accurate LAI estimations. Model-based LAI estimations were
validated against LAI measurements determined non-destructively using an allometric
relationship obtained in this study. The model performance was also compared with LAI estimates
obtained by other classical indirect methods based on bottom-up hemispherical images and gaps
fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The
model performance was slightly better than that of the hemispherical image-based method, which
tended to underestimate LAI. These results show the great potential of the developed model for
near real-time LAI estimation, which can be further improved in the future by increasing the
dataset used to train the model
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