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Projeto de investigação
Centre for Mechanical and Aerospace Science and Technologies
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Publicações
Agricultural practices for biodiversity enhancement: evidence and recommendations for the viticultural sector
Publication . Marcelino, Sara M.; Gaspar, Pedro Dinis; do Paço, Arminda; Lima, Tânia M.; Monteiro, Ana; Franco, José Carlos; Santos, Erika S.; Campos, Rebeca; Lopes, Carlos M.
Agricultural expansion and intensification worldwide has caused a reduction in ecological
infrastructures for insects, herbaceous plants, and vertebrate insectivores, among other organisms.
Agriculture is recognized as one of the key influences in biodiversity decline, and initiatives such as
the European Green Deal highlight the need to reduce ecosystem degradation. Among fruit crops,
grapes are considered one of the most intensive agricultural systems with the greatest economic
relevance. This study presents a compilation of management practices to enhance biodiversity
performance, which applies generally to the agricultural sector and, in particular, to viticulture,
concerning the diversity of plants, semi-natural habitats, soil management, and the chemical control
strategies and pesticides used in agricultural cultivation. Through a critical review, this study
identifies a set of recommendations for biodiversity performance and their corresponding effects,
contributing to the dissemination of management options to boost biodiversity performance. The
results highlight opportunities for future investigations in determining the needed conditions to
ensure both biodiversity enhancement and productive gains, and understanding the long-term effects
of innovative biodiversity-friendly approaches.
Towards sustainable agriculture: A critical analysis of agrobiodiversity assessment methods and recommendations for effective implementation
Publication . Marcelino, Sara M.; Gaspar, Pedro Dinis; do Paço, Arminda; Lima, Tânia M.; Monteiro, Ana; Franco, José Carlos; Santos, Erika S.; Campos, Rebeca; Lopes, Carlos M.
Agriculture intensification has driven the loss of biodiversity at a global level. The imple-
mentation of strategies to conserve and promote biodiversity in agricultural areas can be favoured
by adequate assessment methods that foster the awareness of decision makers about the impact of
management practices. This paper presents a state-of-the-art review of assessment methods of the
overall biodiversity in agricultural systems, focusing on the quantitative methods applied, indicators
of biodiversity, and functionalities. It was concluded that compensation effects and difficulties in in-
terpretation are associated with currently common methodologies of composite indicator calculation
to assess biodiversity performance. This review allowed for the identification and critical analysis
of current methodologies for biodiversity assessments in the agricultural sector, and it highlighted
the need for more implementation-oriented approaches. By providing recommendations on what
should be considered when formulating biodiversity assessment methods, this study can contribute
to the formulation of appropriate assessment frameworks for agricultural management policies
and strategies.
A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities
Publication . Alibabaei, Khadijeh; Gaspar, Pedro D.; Lima, Tânia M.; Campos, Rebeca M.; Girão, Inês; Monteiro, Jorge; Lopes, C.M.
Deep Learning has been successfully applied to image recognition, speech recognition,
and natural language processing in recent years. Therefore, there has been an incentive to apply
it in other fields as well. The field of agriculture is one of the most important fields in which the
application of deep learning still needs to be explored, as it has a direct impact on human well-being.
In particular, there is a need to explore how deep learning models can be used as a tool for optimal
planting, land use, yield improvement, production/disease/pest control, and other activities. The
vast amount of data received from sensors in smart farms makes it possible to use deep learning as a
model for decision-making in this field. In agriculture, no two environments are exactly alike, which
makes testing, validating, and successfully implementing such technologies much more complex
than in most other industries. This paper reviews some recent scientific developments in the field of
deep learning that have been applied to agriculture, and highlights some challenges and potential
solutions using deep learning algorithms in agriculture. The results in this paper indicate that by
employing new methods from deep learning, higher performance in terms of accuracy and lower
inference time can be achieved, and the models can be made useful in real-world applications. Finally,
some opportunities for future research in this area are suggested
Evaluation of a deep learning approach for predicting the fraction of transpirable soil water in vineyards
Publication . Alibabaei, Khadijeh; Gaspar, Pedro D.; Campos, Rebeca M.; Rodrigues, Gonçalo C.; Lopes, Carlos M.
As agriculture has an increasing impact on the environment, new techniques can help meet
future food needs while maintaining or reducing the environmental footprint. Those techniques must
incorporate a range of sensing, communication, and data analysis technologies to make informed
management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel,
labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative
environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was
trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing
region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable
Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative
humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five,
and seven days. The model achieved an RMSE between 8.3% and 16.6% and an R2-score between
0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a
different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an R2-score
of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction
model was compared with that of the Random Forest model, support vector regression, and linear
regression. The results showed that BiLSTM performed better than the RF model on the unseen data,
and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/00151/2020