C-MAST - Center for Mechanical and Aerospace Science and Technologies
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Historically, the research unit “CAST – Centre for Aerospace Science and Technologies” was created in 1994 by a small group of aerospace engineers. Now we became a Reasearch Center developing studies in Energy, Mechanical and Aerospace Engineering with a wide spectrum of areas, from Astrodynamics to Technological Forecasting.
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Browsing C-MAST - Center for Mechanical and Aerospace Science and Technologies by Author "Alibabaei, Khadijeh"
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- Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation SchedulingPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Lima, Tânia M.Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.
- 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.
- Irrigation optimization with a deep reinforcement learning model: Case study on a site in PortugalPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Assunção, Eduardo Timóteo; Alirezazadeh, Saeid; Lima, Tânia M.In the field of agriculture, the water used for irrigation should be given special treatment, as it is responsible for a large proportion of total water consumption. Irrigation scheduling is critical to food production because it guarantees producers a consistent harvest and minimizes the risk of losses due to water shortages. Therefore, the creation of an automatic irrigation method using new technologies is essential. New methods such as deep learning algorithms have attracted a lot of attention in agriculture and are already being used successfully. In this work, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two Long Short Term Memory models were used as the agent environment. One predicts the total water in the soil profile on the next day. The other one was employed to estimate the yield based on the environmental condition during a season and then measure the net return. The agent uses this information to decide the following irrigation amount. An Artificial Neural Network, a Long Short Term Memory, and a Convolutional Neural Network were used to estimating the Q-table during training. Unlike the Long-Short Terms Memory model, the Artificial Neural Network and the Convolutional Neural Network could not estimate the Q-table, and the agent’s reward decreased during training. The comparison of the performance of the model was done with fixed base irrigation and threshold based irrigation. The trained model increased productivity by 11% and decreased water consumption by 20–30% compared to the fixed method.
- Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora ClassificationPublication . Corceiro, Ana; Pereira, Nuno José Matos; Alibabaei, Khadijeh; Gaspar, Pedro DinisThe global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.
- Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A ReviewPublication . Corceiro, Ana; Alibabaei, Khadijeh; Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Pereira, Nuno José MatosThe rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities.
- Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning MethodPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Lima, Tânia M.In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.
- 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.