Nitrogen (N) has commonly been applied in Eucalyptus stands in Brazil and it has a direct relation with biomass production and chlorophyll content. Foliar N concentrations are used to diagnose soil and plant fertility levels and to develop N fertilizer application rates. Normally, foliar N is obtained using destructive methods, but indirect analyses using Vegetation Indexes (VIs) may be possible. The aim of this work was to evaluate VIs to estimate foliar N concentration in three Eucalyptus clones. Lower crown leaves of three clonal Eucalyptus plantations (25 months old) were classified into five color patterns using the Munsell Plant Tissue Color Chart. ).

Estimation of the presence of people in real time is extremely useful for businesses in providing better services while saving money. In this paper, we propose a technique for estimating the number of mobile devices present at a certain place and time, through analysis of WiFi probe requests from smart devices. Our goal is to address the problem through a solution that is immune to Media Access Control (MAC) address randomization strategies. The idea is to make use of information propagated in the environment, without the need to know the real MAC addresses of the devices. A state machine was modeled to detect the arrival, presence, and departure of devices in proximity to the sensors. A hardware prototype was developed for device detection, and its efficiency was evaluated in experiments that involved comparing the results of the proposed method with the manual measurements made by researchers. The proposed method provided very accurate correlations between the number of mobile devices detected and the real number of people in the environment.

Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen (R cv 2 = 0.95), phosphorous (R cv 2 = 0.93), and sulphur concentration (R cv 2 = 0.85). The estimates for concentrations of calcium (R cv 2 = 0.81), magnesium (R cv 2 = 0.22), and potassium (R cv 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy (R cv 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate (R cv 2 = 0.78) using the logarithm transformation. Concentrations of boron (R cv 2 = 0.68) and manganese (R cv 2 = 0.79) were more accurate using the first derivative, while zinc (R cv 2 = 0.31) concentration was most accurate using the second derivative.

The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.