1,720,963 research outputs found
Plant-Wearable Electronics for Climate-Smart Agriculture: Toward Energy-Autonomous Bio-Impedance Monitoring Systems
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Fully Digital Amplitude Estimation System for In-Vivo Stem Impedance Monitoring
Full text linkSmart agriculture aims to improve food production and reduce the waste of water and chemicals by monitoring the crops with sensors. Direct and in-vivo crop monitoring can improve the information extracted and increase the impact of smart agriculture. Here, we propose a system to estimate the amplitude of a signal traveling inside a plant stem in vivo. The amplitude of this signal is strictly related to the impedance of the plant, a promising parameter to monitor plant status. This approach allows monitoring the plant impedance with an electric signal carrying other information. The plant stem will act as a communication channel, removing the need for wireless communication systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Preliminary Steps Towards a Low Power Integrated Circuit for AgriTech: a Relaxation Oscillator for Stem Impedance Monitoring
Full text linkThe main challenges in modern agriculture involve addressing the disruptive environmental effects caused by crop and livestock production, which are essential for human survival. Emerging trends point towards a world of pervasive IoT (Internet of Things) nodes, where low-cost, low-power, and in-vivo plant sensors can detect abiotic and biotic stresses at the earliest stages. This paper presents a preliminary study of an integrable, low-power relaxation oscillator, consisting of a Schmitt trigger in a feedback loop with the plant’s stem to evaluate its health status. Simulations have been conducted to compare the energy consumption of this circuit with state-of-the-art electronic sensors, focusing on energy usage per measurement. The results indicate that this new solution can sense the plant’s health status with an energy consumption below 10 μJ per measurement, which is at least an order of magnitude lower than existing electronic systems found in the literature
Evaluation of Machine Learning Models for Water Stress Detection Using Stem Impedance
No full textFood security, producing enough food for every person on the planet, is becoming a significant issue. Increasing world population and climate change are setting new challenges to food production. Water stress can cause severe damage to crops, and detecting and preventing this threat is crucial. Smart agriculture and the use of sensors directly on the field is a promising and rapidly evolving solution. Data collected by a large number of sensors must be analyzed and efficiently interpreted. In this context, machine learning is an effective solution. This article conducts a comparative analysis of several well-established machine learning models, all trained on a dataset enriched with a novel parameter for the assessment of plant health, the stem electrical impedance (modulus and phase). This feature gives promising results since it is a direct parameter of the plant itself. Moreover, the inclusion of the stem impedance parameter significantly boosted the model's performance, notably enhancing the effectiveness, particularly evident in the case of the top-performing model in this study, the random forest algorithm. When incorporating stem electrical impedance, this model achieved an impressive F1 score of 98%, markedly surpassing the 88% obtained in its absence. As a complementary analysis, a permutation feature performance analysis was conducted, highlighting the potential of stem impedance modulus as a promising feature for evaluating plant watering conditions. The removal of impedance modulus from the training model resulted in an average classification performance loss of 25% in terms of F1 score, suggesting how impedance monitoring is a promising approach for plant health management
Machine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements
Full text linkSmart agriculture is a promising solution to improve food production and reduce waste of resources. The idea is to adopt electronics and sensors to monitor key parameters of the crops and integrate these data with farmer knowledge. Sensors monitor both the environment and the plant itself, generating a huge amount of data. Data processing is a key aspect of smart agriculture, and machine learning can help to understand the data and extract the needed feature. In this paper, we present a performance comparison of several machine learning models trained to detect the water stress condition of plants. The dataset used for this study includes the stem electrical impedance, a novel parameter directly measured on the plants. The machine learning models are compared based on three different metrics, and the average accuracy is higher than 85%. The effect of removing the stem electrical impedance results in worse performance of the models, indicating its impact in the application
In-vivo proximal monitoring system for plant water stress and biological activity based on stem electrical impedance
Full text linkPopulation growth and global warming are the main threats to food production. Food security, producing enough food for the entire population, is becoming harder, and new strategies must be applied. Smart agriculture tackles this problem by integrating field sensors and data with the farmers’ knowledge to increase crop yield and reduce resource waste.This paper proposes a system to monitor the plant water stress status. This system monitors the plant directly and does not rely on environmental sensors. Acquired data are sent to a remote server thanks to LoRa communication. The designed system is low-power and relies on a single battery with more than five years of expected lifetime. The system monitors the trunk electrical impedance of plants thanks to a relaxation oscillator with a portion of the trunk in the feedback loop. This way, changes in the impedance are reflected in changes in the oscillator frequency.Two systems were installed directly in the fields and connected to apple trees. Statistical analyses were performed on the acquired data. The correlation between the trunk frequency values and the soil water potential is above 75% for both plants.The proposed system is low-power and low-cost and could be directly adopted in the fields. It can detect the water status of plants directly, avoiding environmental sensors
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