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- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, HugoO modelo Faster R-CNN tem grande potencial de aplicação na deteção de pêssegos e poderá vir a ser uma boa ferramenta para estimar a producão em pomares, ajudando no planeamento da colheita e do armazenamento da fruta
- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, H.A deteção de frutos é de fundamental importância em sistemas de estimação de produção. Neste trabalho, são apresentados os resultados preliminares da utilização do método de deteção de objetos Faster R-CNN na deteção de imagens de pêssegos. O estudo consiste na avaliação do desempenho do método em imagens RGB obtidas em ambiente real num pomar. Embora este método de deteção tenha sido aplicado noutros trabalhos com o objetivo de detetar frutos, ainda não foi utilizado na deteção de pêssegos. A cor, a sua distribuição na árvore e a clusterização são características intrínsecas aos pêssegos. Os resultados obtidos, ainda que preliminares, mostram um elevado potencial da utilização do método na deteção destes frutos. Todavia, os resultados também mostram a necessidade de melhoria no desempenho. Isso pode ser alcançado com o aumento na quantidade de imagens de treino e também por definir um melhor critério de anotação dos frutos oclusos.
- 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.
- Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular EconomyPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Simões, Maria Paula; Ramos, António; Proença, H.; Inácio, Pedro R. M.Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
- Preliminary results of peach detection in images applying convolutional neuronal networkPublication . Assunção, Eduardo Timóteo; Proença, H.; Veiros, André; Mesquita, Ricardo; Gaspar, Pedro DinisThe fruit detection part is very important for a good performance in a yield estimation system. This paper presents the preliminary results using the object detection Faster R-CNN method in the peaches images. The aim is evaluate the method performance in the detection of peach RGB images. Images acquired in an orchard were used. Although this method of object detection has been applied in other studies to detect fruits, according to the literature, it has not been used to detect peaches. The results, although preliminary, show a great potential of using the method to detect peach.
- Automated Weed Detection Systems: A ReviewPublication . Shanmugam, Saraswathi; Assunção, Eduardo Timóteo; Mesquita, Ricardo; Veiros, André; Gaspar, Pedro DinisA weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control.
- 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.
- Portable, multi-task, on-the-edge and low-cost computer vision framework based on deep learning: Precision agriculture applicationPublication . Assunção, Eduardo Timóteo; Proença, Hugo Pedro Martins Carriço; Gaspar, Pedro Miguel de Figueiredo Dinis OliveiraPrecision agriculture is a new concept that has been introduced worldwide to increase production, reduce labor and ensure efficient management of fertilizers and irrigation processes. Computer vision is an essential component of precision agriculture and plays an important role in many agricultural tasks. It serves as a perceptual tool for the mechanical interface between robots and environments or sensed objects, as well as for many other tasks such as crop yield prediction. Another important consideration is that some vision applications must run on edge devices, which typically have very limited processing power and memory. Therefore, the computer vision models that are to run on edge devices must be optimized to achieve good performance. Due to the significant impact of Deep Learning and the advent of mobile devices with accelerators, there has been increased research in recent years on computer vision for general purpose applications that have the potential to increase the efficiency of precision agriculture tasks. This thesis explore how deep learning models running on edge devices are affected by optimizations, i.e., inference accuracy and inference time. Lightweight models for weed segmentation, peach fruit detection, and fruit disease classification are cases of studies. First, a case study of peach fruit detection with the well-known Faster R-CNN object detector using the breakthrough AlexNet Convolutional Neural Network (CNN) as the image feature extractor is performed. A detection accuracy of 0.90 was achieved using metric Average Precision (AP). The breakthrough AlexNet CNN is not an optimized model for use in mobile devices. To explore a lightweight model, a case study of peach fruit disease classification is next conducted using the MobineNet CNN. The MobileNet was trained on a small dataset of images of healthy, rotten, mouldy, and scabby peach fruit and achieved a performance of 0.96 F1. Lessons learned from this work led to using this model as a baseline CNN for other computer vision applications (e.g., fruit detection and weed segmentation). Next, a study was conducted on robotic weed control using an automated herbicide spot sprayer. The DeepLab semantic segmentation model with the MobileNet backbone was used to segment weeds and determine spatial coordinates for the mechanism. The model was optimized and deployed on the Jetson Nano device and integrated with the robotic vehicle to evaluate real-time performance. An inference time of 0.04 s was achieved, and the results obtained in this work provide insight into how the performance of the semantic segmentation model of plants and weeds degrades when the model is adapted through optimization for operation on edge devices. Finally, to extend the application of lightweight deep learning models and the use of edge devices and accelerators, the Single Shot Detector (SSD) was trained to detect peach fruit in three different varieties and was deployed in a Raspberry Pi device with an integrated Tensor Unity Processor (TPU) accelerator. Some variations of MobileNet as a backbone were explored to investigate the tradeoff between accuracy and inference time. MobileNetV1 yielded the best inference time with 21.01 Frame Per Second (FPS), while MobileDet achieved the best detection accuracy (88.2% AP). In addition, an image dataset of three peach cultivars from Portugal was developed and published. This thesis aims to contribute to future steps in the development of precision agriculture and agricultural robotics, especially when computer vision needs to be processed on small devices.