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- 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.
- Monitorização da Disponibilização de Cobertura Celular Ubíqua em Ambiente Hospitalar: Medições do Espetro Radioelétrico no CHUCBPublication . Silva, Óscar; Teixeira, Emanuel; Corceiro, Ana; Reis, António; Velez, Fernando JoséNos últimos anos, as comunicações moveis celulares e sem fios têm evoluído de forma significativa, possibilitando o surgimento de várias tecnologias que transformaram a forma como nos comunicamos. No contexto da saúde, as Redes Sem Fios de Área Corporal (WBAN) possibilitam a monitorização remota de pacientes, recolhendo e transmitindo dados vitais através de dispositivos localizados ao redor ou dentro do corpo humano. Apesar dos avanços nas tecnologias de comunicação, estudos anteriores indicam que muitos hospitais enfrentam desafios de garantia de uma cobertura adequada, devido à complexidade das suas infraestruturas e às interferências que podem comprometer o sinal. O objetivo deste estudo é avaliar a cobertura celular no Centro Hospitalar Universitário Cova da Beira (CHUCB), identificando áreas onde a melhoria é necessária. Foram realizadas medições do espectro radioelétrico em mais de 20 pontos no CHUCB, utilizando dois equipamentos principais: o analisador de espetro NARDA SRM-3006 e o Scanner R&S®TSME6. As medições abrangeram diversas áreas do hospital, avaliando a potência do sinal recebido e a qualidade da ligação em diferentes horários, ao longo do dia. Os resultados indicam que as operadoras MEO e NOS dominam a cobertura celular no CHUCB, embora áreas como os pontos 17, 19 e 21 de medição, necessitem de melhorias significativas. Para a tecnologia 5G NR, a cobertura predominante em quase todos os pontos de medição pertence à operadora MEO, enquanto para a tecnologia LTE, a operadora NOS apresentou a melhor qualidade de sinal. Com base nos dados obtidos, propõe-se a instalação de femtocélulas nas áreas identificadas como deficientes para melhorar a cobertura celular no CHUCB.