Abstract
A high-resolution advanced laser induced breakdown spectroscopy prototype was used to quantify lithium (Li) in lithiniferous rocks. Samples were collected from Barroso's mine (Portugal), claimed as Western Europe's largest spodumene Li discovery. 51 samples from a reverse circulation drill were collected, one for each meter interval, dried, milled, pressed into pellets and further analyzed by laser induced breakdown spectroscopy. Quantification was attempted using either linear models based on the intensity of selected Li spectral lines or advanced chemometrics methods. The latter was very successful, with correlation coefficients of 0.97 against certified laboratory results.

Abstract
A low-computational intensive laser control approach is proposed for implementing an embedded control system, using pattern recognition by relevant principal component analysis for laser induced breakdown spectroscopy applications. The laser energy is directly related to the resulting spectral pattern and is determined by iterations in the feature space. Results show that single shot iterations until optimum energy can be significantly reduced by pattern recognition. A performance benchmark with minerals, alloys and pellets from material collected from a drill demonstrated an average of 50% improvement, significantly reducing sample deterioration and improving measurement safety.

Abstract
Spectral information is characterized by multi-scaled interference, convolution and variability. Spectral lines are fragmented and diffused along the spectra. In many cases, matrix and physical effects do not allow to determine specific bands. Despite this limitation, the observed spectra contains significant amounts of information about the sample composition and characteristics, which once understood, can make spectroscopy an ideal technology for analyzing complex samples, such as bodyfluids and tissues. Breaking down and deciphering the structure of spectral information is paramount for the development of reagent-free point-of-care devices. A self-learning artificial intelligence was developed to take advantage of spectral complex information structure. It determines the relationships between composition and/or spectral features in high-dimensional space, where different sub-spaces correlate to specific constituents or characteristics. It also establishes a knowledgebase, by feature space transformations and optimizing co-variance search direction under the correct 'matrix effect' context. An example of hemogram analysis with erythrocyte and leucocyte counts is presented to demonstrate the advantages of the developed methodology.

Abstract
Soil moisture (SM) at three depths (15, 25, and 30 cm), derived from the optical trapezoidal model (OPTRAM), was used for multiyear, multisite monitoring of agricultural droughts over two agricultural crops (Maize and Soybean) in southern Mozambique. The OPTRAM was implemented using satellite data from Sentinel-2 and was validated against field SM assessed by gravimetric methods and by Watermark Sensors in sandy-soils with very low water holding capacity (0.13 cm(3)/cm(3)). The OPTRAM model estimated the SM at 15 and 25 cm yielding a R-2 >= 0.79 and RMSE <= 0.030 cm(3)/cm(3). The OPTRAM-derived SM was successfully used as input to compute and map the soil water deficit index, an indicator of agricultural drought. The results indicate that OPTRAM can provide useful information to improve water productivity in cropland under the specific conditions of Mozambique agricultural systems and for early warning systems development. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
The dynamic effects of kaolin clay particle film application on the temperature and spectral reflectance of leaves of two autochthonous cultivars (Touriga Nacional (TN, n=32) and Touriga Franca (TF, n=24)) were studied in the Douro wine region. The study was implemented in 2017, in conditions prone to multiple environmental stresses that include excessive light and temperature as well as water shortage. Light reflectance from kaolin-sprayed leaves was higher than the control (leaves without kaolin) on all dates. Kaolin's protective effect over leaves' temperatures was low on the 20 days after application and ceased about 60 days after its application. Differences between leaves with and without kaolin were explained by the normalized maximum leaf temperature (T_max_f_N), reflectance at 400 nm, 532 nm, and 737 nm, as assessed through TN data. The wavelengths of 532 nm and 737 nm are associated with plant physiological processes, which support the selection of these variables for assessing kaolin's effects on leaves. The application of principal component analysis to the TF data, based on these four variables (T_max_f_N and reflectances: 400, 532, 737 nm) selected for TN, explained 83.56% of data variability (considering two principal components), obtaining a clear differentiation between leaves with and without kaolin. The T_max_f_N and the reflectance at 532 nm were the variables with a greater contribution for explaining data variability. The results improve the understanding of the vines' response to kaolin throughout the grapevine cycle and support decisions about the re-application timing.

Abstract
An Automatic Calibration of Fertilizers (ACFert) system was developed, for use with centrifugal, pendulum or other types of broadcast spreaders which distribute dry granular agricultural materials on the top of the soil. The ACfert is based on image processing techniques and includes a specially designed mat, which should be placed in the ground for spreaders calibration. A set of images acquired outdoor by a standard device (simple camera) is used to extract information about the spreader distribution pattern. Each image is processed independently, providing as output two numerical values for each grid element present in the image - the number of fertilizers/seeds counted, and its numerical label. The performance of ACFert was evaluated for automatic granules detection using a set of manual counting measurements of nitrate fertilizer and wheat seeds. A total of 185 images acquired with two mobiles devices were used with a total of 498 quadrilateral elements observed and analysed. The overall mean absolute relative error between counting and computed by the ACFert system, were 0.75 +/- 0.75% for fertilizer and 2.12 +/- 1.68% for wheat. This near real-time calibration tool is a very low cost system that can be easily used on field, providing results to support accurate spreader calibration in near real time for different types of fertilizers or seeds.