Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from trees through video footage filmed in line along a row of orange trees on a plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object-tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. Being the scientific and technological innovation the possibility of distinguishing the green color of the fruits from the green color of the leaves. The use of YOLOv4 together with object detectors managed to reduce the number of double counting errors and obtained a count close to the actual number of oranges visible in the video. The results were promising, with an mAP50 of 80.16%, mAP50:95 of 53.83%, precision of 0.92, recall of 0.93, F1-score of 0.93, and average IoU of 82.08%. Additionally, the counting algorithm successfully identified and counted 204 oranges, closely approaching the actual count of 208. The study also resulted in a database with an amount of 644 images containing 43,109 orange annotations that can be used in future works.

The monitoring of water resources through conventional methods, related to a manual process when performing the sample collection, followed by laboratory analysis, presents some difficulties concerning the logistics of the process, such as access to the interior of a lake, in addition to often being based on a small number of samples. The concept of the internet of things (IoT) is used here to collect data through five parametric probes contained in the floating station located inside a lake and inform them in real time continuously. The main objective of this research is to demonstrate the applicability of the IoT concept in the continuous monitoring of water in a lentic environment. Therefore, it is necessary to develop a tool for this. Upon reaching this objective, the advantages observed in this research confirmed that the IoT paradigm is an essential resource, justifying a natural tendency to establish itself when there is a need to collect data efficiently and continuously. Furthermore, the experimental result proves the IoT concept’s efficiency, agility, and reliability to environmental issues, especially regarding the most significant natural and indispensable resource for the planet, water.

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.

The purpose of this study is to employ and improve evolutionary algorithms, namely the genetic algorithm (GA) and the differential evolution algorithm (DE), to extract the parameters of the equivalent circuit model (ECM) of a bifacial photovoltaic module using the representative model of a diode with five parameters (1D5P). The objective is to simulate the characteristics of the I–V curves for various irradiation and temperature scenarios. A distinctive feature of this study is the exclusive use of the information in the technical sheet of the bifacial module to conduct the entire extraction and simulation process, eliminating the need to resort to external sources of data or experimental data. To validate the methods, a comparison was made between the simulation results and the data provided by the bifacial module manufacturer, contemplating different scenarios of irradiation and temperature. The DE was the most accurate algorithm for the 1D5P model, which presented a maximum average error of 1.57%. In comparison, the GA presented a maximum average error of 1.98% in the most distant scenario of STC conditions. Despite the errors inherent to the simulations, none of the algorithms presented relative errors greater than 8%, which represents a satisfactory modeling for the different operational conditions of the bifacial photovoltaic modules.

Introduction: Cannabidiol (CBD) has become a promising bioactive for the next decades after the recent recognition of the medical potential of Cannabis derivatives by United Nations member countries, as it has no psychotropic potential as your isomer ∆9-tetrahydrocannabinol (∆9-THC). The differentiation of these isomers has been studied for decades. Recent studies demonstrate that even with more subtle chemical characteristics, such as those of the CBD enantiomers, there are consider-able bioactive differences. However, there are still not many studies on their chemical structures. Aim: This work aims to present experimental data obtained by Nuclear Magnetic Resonance (NMR) to better elucidate the three-dimensional structure of this enantiomeric bioactive. Materials and methods: For this, a sample of non-synthetic high purity CBD was subjected to different one-dimensional (1D-NMR) and two-dimensional (2D-NMR) analyses related to the hydrogen (1H) and carbon (13C) nuclei. Results and discussion: The 1D-NMR techniques used are sufficient to distinguish the CBD and ∆9-THC isomers, but not to identify the enantiomeric characteristics of the non-synthetic CBD. Conclusions: It is concluded that the two-dimensional homonuclear (1H,1H) and heteronuclear (1H,13C) techniques analyzed are suitable to help distinguish CBD enantiomers.