Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.info:eu-repo/semantics/publishedVersio

Insect detection in sticky trap images of tomato crops using machine learning

As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5 for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach was used to minimize insect detection duplicates in a non-complex way. This article also contributes to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for mAP_0.5, with a precision and recall of 88% and 91%, respectively, using YOLOv5x.info:eu-repo/semantics/publishedVersio

Meta-pipeline: A new execution mechanism for distributed pipeline processing

The Caravela platform has been proposed by the authors of this paper to perform distributed stream-based computing on general purpose computation. This platform uses a secured execution unit called flow-model that prevents remote users to touch local information in a computer. The flow-model is assigned to local or remote processing units that execute its program. This paper is focused on a new execution mechanism that defines a pipeline composed by flow-models, called meta-pipeline, and is designed as a set of additional functions of the Caravela platform. The pipeline is executed automatically by the meta-pipeline runtime environment. This paper describes the execution mechanism and also presents an application example.info:eu-repo/semantics/acceptedVersio

Deep learning-based graffiti detection: A study using Images from the streets of Lisbon

This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.info:eu-repo/semantics/publishedVersio

Short-reach MCF-based systems employing KK Receivers and feedforward neural networks for ICXT mitigation

This paper proposes and evaluates the use of machine learning (ML) techniques for mitigating the effect of the random inter-core crosstalk (ICXT) on 256 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers–Kronig (KK) receivers. The performance improvement provided by the k-means clustering, k nearest neighbor (KNN) and feedforward neural network (FNN) techniques are assessed and compared with the system performance obtained without employing ML. The FNN proves to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features for the training phase. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew×symbol rate≪1). By employing the proposed FNN, the bit error rate (BER) always stood below 10−1.8 on all the time fractions under analysis (compared with 100 out of 626 occurrences above the BER threshold when ML was not used). For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed FNN shows a received optical power improvement of almost 3 dB.info:eu-repo/semantics/publishedVersio

Measuring the extent of convective cores in low-mass stars using Kepler data: towards a calibration of core overshooting

Our poor understanding of the boundaries of convective cores generates large uncertainties on the extent of these cores and thus on stellar ages. Our aim is to use asteroseismology to consistently measure the extent of convective cores in a sample of main-sequence stars whose masses lie around the mass-limit for having a convective core. We first test and validate a seismic diagnostic that was proposed to probe in a model-dependent way the extent of convective cores using the so-called $r_{010}$ ratios, which are built with $l=0$ and $l=1$ modes. We apply this procedure to 24 low-mass stars chosen among Kepler targets to optimize the efficiency of this diagnostic. For this purpose, we compute grids of stellar models with both the CESAM2k and MESA evolution codes, where the extensions of convective cores are modeled either by an instantaneous mixing or as a diffusion process. Among the selected targets, we are able to unambiguously detect convective cores in eight stars and we obtain seismic measurements of the extent of the mixed core in these targets with a good agreement between the CESAM2k and MESA codes. By performing optimizations using the Levenberg-Marquardt algorithm, we then obtain estimates of the amount of extra-mixing beyond the core that is required in CESAM2k to reproduce seismic observations for these eight stars and we show that this can be used to propose a calibration of this quantity. This calibration depends on the prescription chosen for the extra-mixing, but we find that it should be valid also for the code MESA, provided the same prescription is used. This study constitutes a first step towards the calibration of the extension of convective cores in low-mass stars, which will help reduce the uncertainties on the ages of these stars.Comment: 27 pages, 15 figures, accepted in A&

Nuno Portas and the Spanish influence on the definition of housing policies in Portugal in the period of democratic transition

Taking the housing crisis and the SAAL program as a central interest point of architects and sociologists in the aftermath of the Portuguese revolution, this chapter tracks the influence of Spanish architecture in Portugal and the relations of Portuguese and Spanish architects, signaling the main role of Nuno Portas. It begins by introducing the background of the architecture exchange between Portugal and Spain, since the 1960’s, through the diffusion and interchange activities of Nuno Portas (section 2). It continues to discuss the role of architects on urban change during the revolutionary process from the viewpoint Joan Antonio Solans experiences and writings (Section 3). Then it takes on the social movements debate with Manuel Castells reflections and writings about the new housing policies and experiences (Section 4). Finally, a short reflection on the interchange of ideas and experiences between Portugal and Spain in presented in the conclusion (Section 5).info:eu-repo/semantics/submittedVersio

Medida de desumanização baseada em traços: adaptação para a população Portuguesa

Although dehumanization (i.e., the denial of full humanness to others; Haslam, 2006) has been a frequent subject in social psychology, a set of traits designed to evaluate this phenomenon has not been validated to the Portuguese population. The main purpose of this study was to translate, culturally adapt and validate a set of dehumanization traits proposed by Haslam and colleagues (Haslam & Bain, 2007; Haslam, Bain, Douge, Lee & Bastian, 2005), which measure both the denial of uniquely human and human nature traits. A sample of 597 individuals (Mage = 40.83; SD = 11.50) were asked to rate a set of 52 traits on how much they perceived each as a characteristic of human nature and human uniqueness, as well as its desirability. T-tests were conducted to distinguish between low and high rated traits in each dimension, and to construct clusters of traits that differ in each dimension. We successfully provide a measure containing positive traits in both senses of humanness dimensions; however, we were only able to validate a human uniqueness measure with negative valence. Implications of this measure for future research on dehumanization processes are discussed.info:eu-repo/semantics/publishedVersio

Attachment and adaptation to breast cancer: The mediating role of avoidant emotion processes

Attachment insecurity is associated with difficulties in adapting to cancer. Accumulating evidence points to the influence of avoidant emotion processes in this association. This study explored this pathway by examining the association between attachment insecurity and quality of life in women with breast cancer, and by exploring the mediating role of two avoidant emotion processes in this association. Women with breast cancer (N = 155) completed measures of attachment, emotional suppression, emotional awareness and quality of life. Avoidance of attachment was positively associated with emotional suppression (β = .29, p \u3c .01) and lack of emotional awareness (β = .27, p \u3c .01), and negatively associated with quality of life (β = −.22, p \u3c .05). Lack of emotional awareness partially mediated the relationship between attachment avoidance and quality of life (indirect effect β = −.12, p = .008). Attachment anxiety was not associated with any variable. Attachment avoidance may hinder the process of adaptation to breast cancer and difficulties in identifying and describing emotions may be partly responsible for this influence. Access to and ability to benefit from social and medical supports is likely to depend on being able to engage with others and recognise and process emotions effectively. Research and clinical implications are discussed