Optical gas sensing with liquid crystal droplets and convolutional neural networks

UIDB/50009/2020 UIDB/ 04378/2020 SCENT-ERC-2014-STG-639123, 2015-2022Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9–4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.publishersversionpublishe

Material Recognition in the Wild with the Materials in Context Database

Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.Comment: CVPR 2015. Sean Bell and Paul Upchurch contributed equall

Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling

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.Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).info:eu-repo/semantics/publishedVersio

Application of Artificial Intelligence algorithms to support decision-making in agriculture activities

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 in which the application of artificial intelligence algorithms, and particularly, of deep learning 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 for decision-making 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 sectors. Recent scientific developments in the field of deep learning, applied to agriculture, are reviewed and some challenges and potential solutions using deep learning algorithms in agriculture are discussed. 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. The ability of artificial neural networks, specifically 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 mean square error (MSE) values ranging from 0.07 to 0.27 (mm d–1)² for ETo (Reference Evapotranspiration) and 0.014 to 0.056 (m³m–3)² for SWC (Soil Water Content), with R2 values 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 (MSE) as loss function performed better than the model with other loss functions. Afterwards, the capabilities of these models and their extension, BLSTM and Bidirectional Gated Recurrent Units (BGRU) to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate endof- season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The BLSTM network outperformed the GRU, the LSTM, and the BGRU networks 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 kg/ha. The performance of the BLSTM in the test was compared with the most commonly used deep learning method called CNN, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest regression. The BLSTM out-performed the other models with a R2-score between 0.97 and 0.99. The results show that analyzing agricultural data with the LSTM model improves the performance of the model in terms of accuracy. The CNN 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. Additionally, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two LSTM models trained previously 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. LSTM and CNN networks were used to estimate the Q-table during training. Unlike the LSTM model, the ANN and the CNN could not estimate the Qtable, 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% to 30% compared to the fixed method. Also, an on-policy model, Advantage Actor–Critic (A2C), was implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model A2C reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high importance in Portugal, such as fruit, cereals, and grapevines, which also have large water requirements. The models developed along this thesis can be re-evaluated and trained with historical data from other cultures with high production in Portugal, such as fruits, cereals, and grapes, which also have high water demand, to create a decision support and recommendation system that tells farmers when and how much to irrigate. This system helps farmers avoid wasting water without reducing productivity. This thesis aims to contribute to the future steps in the development of precision agriculture and agricultural robotics. The models developed in this thesis are relevant to support decision-making in agricultural activities, aimed at optimizing resources, reducing time and costs, and maximizing production.Nos últimos anos, a técnica de aprendizagem profunda (Deep Learning) foi aplicada com sucesso ao reconhecimento de imagem, reconhecimento de fala e processamento de linguagem natural. Assim, tem havido um incen tivo para aplicá-la também em outros sectores. O sector agrícola é um dos mais importantes, em que a aplicação de algoritmos de inteligência artificial e, em particular, de deep learning, precisa ser explorada, pois tem impacto direto no bem-estar humano. Em particular, há uma necessidade de explorar como os modelos de aprendizagem profunda para a tomada de decisão podem ser usados como uma ferramenta para cultivo ou plantação ideal, uso da terra, melhoria da produtividade, controlo de produção, de doenças, de pragas e outras atividades. A grande quantidade de dados recebidos de sensores em explorações agrícolas inteligentes (smart farms) possibilita o uso de deep learning como modelo para tomada de decisão nesse campo. Na agricultura, não há dois ambientes iguais, o que torna o teste, a validação e a implementação bem-sucedida dessas tecnologias muito mais complexas do que na maioria dos outros setores. Desenvolvimentos científicos recentes no campo da aprendizagem profunda aplicada à agricultura, são revistos e alguns desafios e potenciais soluções usando algoritmos de aprendizagem profunda na agricultura são discutidos. Maior desempenho em termos de precisão e menor tempo de inferência pode ser alcançado, e os modelos podem ser úteis em aplicações do mundo real. Por fim, são sugeridas algumas oportunidades para futuras pesquisas nesta área. A capacidade de redes neuronais artificiais, especificamente Long Short-Term Memory (LSTM) e LSTM Bidirecional (BLSTM), para modelar a evapotranspiração de referência diária e o conteúdo de água do solo é investigada. A aplicação destas técnicas para prever estes parâmetros foi testada em três locais em Portugal. Um BLSTM de camada única com 512 nós foi selecionado. A otimização bayesiana foi usada para determinar os hiperparâmetros, como taxa de aprendizagem, decaimento, tamanho do lote e tamanho do ”dropout”. O modelo alcançou os valores de erro quadrático médio na faixa de 0,014 a 0,056 e R2 variando de 0,96 a 0,98. Um modelo de Rede Neural Convolucional (CNN – Convolutional Neural Network) foi adicionado ao LSTM para investigar uma potencial melhoria de desempenho. O desempenho decresceu em todos os conjuntos de dados devido à complexidade do modelo. O desempenho dos modelos também foi comparado com CNN, algoritmos tradicionais de aprendizagem máquina Support Vector Regression e Random Forest. O LSTM obteve o melhor desempenho. Por fim, investigou-se o impacto da função de perda no desempenho dos modelos propostos. O modelo com o erro quadrático médio (MSE) como função de perda teve um desempenho melhor do que o modelo com outras funções de perda. Em seguida, são investigadas as capacidades desses modelos e sua extensão, BLSTM e Bidirectional Gated Recurrent Units (BGRU) para prever os rendimentos da produção no final da campanha agrícola. Os modelos usam dados históricos, incluindo dados climáticos, calendário de rega e teor de água do solo, para estimar a produtividade no final da campanha. A aplicação desta técnica foi testada para os rendimentos de tomate e batata em um local em Portugal. A rede BLSTM superou as redes GRU, LSTM e BGRU no conjunto de dados de validação. O modelo foi capaz de captar a relação não linear entre dotação de rega, dados climáticos e teor de água do solo e prever a produtividade com um MSE variando de 0,07 a 0,27 (mm d–1)² para ETo (Evapotranspiração de Referência) e de 0,014 a 0,056 (m³m–3)² para SWC (Conteúdo de Água do Solo), com valores de R2 variando de 0,96 a 0,98. O desempenho do BLSTM no teste foi comparado com o método de aprendizagem profunda CNN, e métodos de aprendizagem máquina, incluindo um modelo Multi-Layer Perceptrons e regressão Random Forest. O BLSTM superou os outros modelos com um R2 entre 97% e 99%. Os resultados mostram que a análise de dados agrícolas com o modelo LSTM melhora o desempenho do modelo em termos de precisão. O modelo CNN obteve o segundo melhor desempenho. Portanto, o modelo de aprendizagem profunda tem uma capacidade notável de prever a produtividade no final da campanha. Além disso, uma Deep Q-Network foi treinada para programação de irrigação para a cultura do tomate. O agente foi treinado para programar a irrigação de uma plantação de tomate em Portugal. Dois modelos LSTM treinados anteriormente foram usados como ambiente de agente. Um prevê a água total no perfil do solo no dia seguinte. O outro foi empregue para estimar a produtividade com base nas condições ambientais durante uma o ciclo biológico e então medir o retorno líquido. O agente usa essas informações para decidir a quantidade de irrigação. As redes LSTM e CNN foram usadas para estimar a Q-table durante o treino. Ao contrário do modelo LSTM, a RNA e a CNN não conseguiram estimar a tabela Q, e a recompensa do agente diminuiu durante o treino. A comparação de desempenho do modelo foi realizada entre a irrigação com base fixa e a irrigação com base em um limiar. A aplicação das doses de rega preconizadas pelo modelo aumentou a produtividade em 11% e diminuiu o consumo de água em 20% a 30% em relação ao método fixo. Além disso, um modelo dentro da táctica, Advantage Actor–Critic (A2C), é foi implementado para comparar a programação de irrigação com o Deep Q-Network para a mesma cultura de tomate. Os resultados mostram que o modelo de táctica A2C reduziu o consumo de água consumo em 20% comparado ao Deep Q-Network com uma pequena mudança na recompensa líquida. Estes modelos podem ser desenvolvidos para serem aplicados a outras culturas com elevada produção em Portugal, como a fruta, cereais e vinha, que também têm grandes necessidades hídricas. Os modelos desenvolvidos ao longo desta tese podem ser reavaliados e treinados com dados históricos de outras culturas com elevada importância em Portugal, tais como frutas, cereais e uvas, que também têm elevados consumos de água. Assim, poderão ser desenvolvidos sistemas de apoio à decisão e de recomendação aos agricultores de quando e quanto irrigar. Estes sistemas poderão ajudar os agricultores a evitar o desperdício de água sem reduzir a produtividade. Esta tese visa contribuir para os passos futuros na evolução da agricultura de precisão e da robótica agrícola. Os modelos desenvolvidos ao longo desta tese são relevantes para apoiar a tomada de decisões em atividades agrícolas, direcionadas à otimização de recursos, redução de tempo e custos, e maximização da produção.Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST). It was also supported by the R&D Project BioDAgro – Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST - Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

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.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio

Spoilage Detection in Raspberry Fruit Based on Spectral Imaging Using Convolutional Neural Networks

Effective spoilage detection of perishable food items like fruits and vegetables is essential for retailers who stock and sell large quantities of these items. This research is aimed at developing a non-destructive, rapid and accurate method which is based on Spectral Imaging (SI) used in tandem with Convolutional Neural Network (CNN) to predict whether the fruit is fresh or rotten. The study also aims to determine the number of days before which the fruit rots. This research employs a primary, quantitative and inductive methods to investigate the Deep Learning based approach to detect fruit spoilage. Raspberry fruit in particular has been chosen for the experiment. Baskets of raspberries from three different stores were bought and stored in the refrigerator at four-degree Celsius. Images of these baskets was captured on a daily basis using an RGB digital camera until all the baskets of fruits were rotten. The study employs a Supervised learning-based classification approach where-by the data is labelled based on the physical appearance of fruits in the basket. The results show that a Spectral imaging technique used along with a CNN yields a good accuracy of 86% with the F1 score of 0.82 to classify the fruits as Good or Bad but does not fare well in estimating the number of days before the fruit actually rots. The ability of CNN to process and identify patterns in a SI to detect spoilage in fruits would help fruit retail operators to optimize their business chain

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)