Browsing by Author "Lopes, Carlos M."
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- Evaluation of a deep learning approach for predicting the Fraction of Transpirable Soil Water in vineyardsPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; 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.
- A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural ActivitiesPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Lima, Tânia M.; Campos, Maria Do Rosario Castiço De; Girão, Inês; Monteiro, Jorge; Lopes, Carlos 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.