Mohamed Sadok Gastli

Mohamed Sadok Gastli

AI & CV Engineer

Publications

You can also find my articles on my Google Scholar profile.

Deep Learning Models for Strawberry Yield and Price Forecasting Using Satellite Images

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021

Forecasting crop yields and prices is crucial for both global food security and providing farmers with valuable information to avoid a price crash. This work proposes a hybrid deep learning model that uses satellite images to forecast strawberry yield along with farmers’ prices, applied in three counties in California. For tractability, a dimensionality reduction technique is applied by converting the images to histograms representing the pixel frequency. The models tested are Convolutional Neural Network (CNN), Variational AutoEncoder (VAE), CNN-Long Short-Term Memory (CNN-LSTM), Stacked AutoEncoder (SAE), and a voting ensemble of CNN-LSTM and SAE. It is found that the proposed voting ensemble of CNN-LSTM and SAE is the best at forecasting the daily strawberry yields and prices in all three counties. Based on an aggregated performance measure (AGM), the voting ensemble model outperforms the models suggested in literature with up to 70% forecasting improvement compared to the CNN model and up to 22% improvement over the CNN-LSTM model.

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Transfer Learning Application for Berries Yield Forecasting using Deep Learning

Published in International Joint Conference on Neural Networks (IJCNN), 2021

To overcome the computational complexity of retraining Deep Learning (DL) yield forecasting models for each type of Fresh Produce (FP), it is necessary to have a generalization of the models’ application to similar FP. This can be done by transferring the learning among similar FP with minimal retraining. Hence, Transfer Learning (TL) is used in this work amongst berries which are similar in nature. First, the proposed DL model is trained using station-based data and satellite images as inputs mapped to the strawberry yield as output. The weights obtained from this learning are transferred to the raspberry yield forecasting model since raspberry and strawberry yields are similar and are both planted in California. The proposed model is an ensemble of two models: the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound DL components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM); trained and tested using station-based data as input and the corresponding strawberry yields as output. Second, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble of Convolutional Neural Network LSTM (CNN-LSTM) models; trained and tested using satellite images as input mapped to the same yields as output. The weights obtained are transferred to the raspberry yield forecasting ensemble model with minimal retraining. It is found that the voting ensemble improves performance by 27% compared to the best performing component model. Based on an aggregated measure, the performance obtained from TL is comparable to that obtained by training the models on the raspberry data without TL, while having around 55 % reduction in processing time.

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Satellite Images and Deep Learning Tools for Crop Yield Prediction and Price Forecasting

Published in International Joint Conference on Neural Networks (IJCNN), 2021

The ability to predict crop yield is vital for food security worldwide and forecasting crop prices can help farmers avoid price crash. In this work, an investigation of using satellite images and deep learning models to predict crop yields as well as forecasting farmers’ prices is conducted. For tractability, dimensionality reduction is achieved by converting the images to histograms representing the pixel frequency. The models tested are LSTM, CNN, CNN-LSTM, CNN-LSTM ensemble as well as a Gaussian Process added to each for enhanced performance. It is found that the proposed ensemble of CNN-LSTMs is the best in predicting the yearly soybean yields in addition to forecasting the daily strawberry yields and prices. It outperforms models suggested in the literature with an improvement of 31% in terms of average Root Mean Square Error (RMSE).

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Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters

Published in Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE), 2021

Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modeling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTMSeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att- CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of Santa Barbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional Neural Network LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts. Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices.

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IoT-Based Fall and ECG Monitoring System: Wireless Communication System Based Firebase Realtime Database

Published in IEEE SmartWorld Congress, 2019

Monitoring elderlies living alone has been a rising issue that caregivers are interested in solving since many elderlies are at risk of experiencing a fall. In the absence of urgent help, serious consequences may occur. This paper presents a complete communication system to monitor elderlies by checking their Electrocardiogram (ECG) and accelerometer data through a cloud-based server anytime on a mobile application ensuring that they are unharmed. This has been implemented by having a Multi-core Processing Unit (MPU), acting as a gateway, at the elderly’s side monitoring signals coming from a wearable sensing device. It will classify ECG and accelerometer data using Machine Learning algorithms, stream the data upon request, alert caregivers through a mobile application and store the data on the database for further analysis in case of a fall. Fall detection had an accuracy of 95% using Extended Nearest Neighbor (E-NN) learning algorithm.

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