Percorrer por autor "Baglat, Preety"
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- Approaches for banana harvesting automationPublication . Baglat, Preety; Dias, Fernando Manuel Rosmaninho Morgado Ferrão; Mendonça, Fábio; Shahnawaz, ShekhA colheita de bananas ainda depende de um pequeno grupo de especialistas, e o seu con hecimento é difícil de transmitir. Este trabalho transforma esse conhecimento numa solução de ponta de aprendizagem automática para decisões de colheita, mais consistente, mais fácil de escalar e de replicar. Primeiro, o estudo de revisão sistemática resumiu as técnicas exis tentes de deep learning e aprendizagem automática disponíveis para identificar os estádios de maturação da banana. Em seguida, procedeu-se à recolha de um conjunto de dados em diferentes campos na Ilha da Madeira, Portugal, sob variadas condições ambientais, com o objetivo de resolver o problema da deteção do cacho de banana e da classificação para col heita. Depois, utilizando imagens de deteção de cachos e imagens rotuladas por especialistas quanto à prontidão para a colheita, recolhidas nos campos, o sistema combina um detetor baseado em You Only Look Once (YOLO) e um classificador You Only Look Once (YOLO) melhorado com Bloco Squeeze-and-Excitation (SE). O sistema de deteção foca-se no cacho principal de banana em cada fotografia para que outros cachos ou fundos complexos não induzam o resultado em erro, e o classificador decide então se esse cacho está pronto para a colheita ou não. O modelo de deteção alcançou 93%,AP50test a cerca de 5,1 ms por im agem, e o modelo de classificação atingiu 94% de precisão a cerca de 2,8 ms por imagem. Uma aplicação Android liga estas partes num fluxo simples e fornece decisões de colheita, nomeadamente Cortar ou Manter. Uma opção Discordar, que envia imagens para o servidor para revisão caso o trabalhador da colheita não concorde com o resultado, ajuda a aumentar a precisão futura do modelo. Foram recolhidos comentários de colhedores e utilizadores du rante os testes no campo, num dia de colheita, para melhorar ainda mais o sistema. Os testes de campo mostraram um desempenho fiável, decisões mais rápidas e menos trabalho manual. Permanecem limitações porque os dados provêm de uma só região e principalmente de um único tipo de banana, e as fotografias não conseguem captar o toque ou todos os parâmetros do campo. Trabalhos futuros deverão incluir estudos de validação com conjuntos de dados de múltiplas regiões geográficas e diversas cultivares de banana, extensão a outras culturas e integração de registos de campo ou diários de colheita para registo automático das avaliações de maturidade, permitir a programação preditiva da colheita e apoiar a documentação de controlo de qualidade para certificações.
- Machine learning system for commercial banana harvestingPublication . Hayat, Ahatsham; Baglat, Preety; Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Baglat, Preety; Silva Mendonça, Fábio Rúben; Morgado-Dias, FernandoAbstract The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learning could significantly impact the visual inspection process domains, allowing consistent harvesting and grading. To achieve the goal of the industry-level harvesting process, this work collects data from professional harvesters from the industry. It investigates six state-of-the-art architectures to find the best solution. 2,685 samples were collected from four different sites with expert opinions from industry harvesters to cut (or harvest) and keep (or not harvest) the banana brunch. Comparative results showed that the DenseNet121 architecture outperformed the other examined architectures, reaching a precision, recall, F1 score, accuracy, and specificity of 85%, 82%, 82%, 83%, and 83%, respectively. In addition, an understanding of the underlying black box nature of the solution was visualized and found adequate. This visual interpretation of the model supports human expert’s criteria for harvesting. This system can assist or replace human experts in the field.
- Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic ReviewPublication . Baglat, Preety; Hayat, Ahatsham; Mendonça, Fábio; Gupta, Ankit; Mostafa, Sheikh Shanawaz; Dias, Fernando MorgadoThe ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.
- Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray ImagesPublication . Hayat, Ahatsham; Baglat, Preety; Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Dias, Fernando MorgadoThe number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.