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Fires in photovoltaic (PV) electrical systems are a real and serious problem because this phenomenon can have severe consequences for the safety of people and the environment. In some cases, fires result from a lack of maintenance or improper installation of PV modules. It is essential to consider prevention and continuous monitoring of the electrical parameters to minimize these risks, as these factors increase the temperature of the photovoltaic modules. The use of thermal analysis techniques can prevent hotspots and fires in photovoltaic systems; these techniques allow detecting and correcting problems in the installation, such as shadows, dirt, and poor-quality connections in PVs. This paper presents a case study of the implementation of thermal analysis in an installation of photovoltaic modules connected to a solar pumping system to identify the formation of hotspots through thermal images using an unmanned aerial vehicle (UAV). Here, a novel methodology is proposed based on the comparison of temperature increases concerning the values of short circuit current, open circuit voltage, and real efficiency of each PV module. In addition, an electrical safety methodology is proposed to design a photovoltaic system that prevents fires caused by hotspots, contemplating critical parameters such as photovoltaic power, number of photovoltaic modules, DC:AC conversion ratio, electrical conductor selection, control devices, and electrical protection; the performance power expected was obtained using standard power test conditions, including irradiance factor, photovoltaic module (PVM) temperature factor, and power reduction factor. [ABSTRACT FROM AUTHOR]

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms. [ABSTRACT FROM AUTHOR]

The presence of pesticide residues in vegetable and fruit products, as a consequence of inappropriate application in some cases, constitutes a risk to the health of the exposed population. In Mexico, the official norm, NOM-003-STPS-1999, only allows the use of pesticides with the phytosanitary registry, the responsible state commission for the control of the process and use of pesticides and toxic substances, which recommends doses and permitted crops. Despite the above, it is still common to find pesticide residues in some vegetable products. In this study, the following were detected: Chlorpyriphos, Dimetomorph I, Malathion, Omethoate, Carbendazim, and Imidacloprid in Nopal. The study was carried out in two collection centers located in the state of Morelos. In total, sixty samples were taken, thirty for each collection center, for a period of 10 months. To determine the pesticide residues, the analytical methodology was used, according to the guide, SANTE/11945/2015; in a laboratory accredited by the Mexican Accreditation Entity A. C. in the norm, NMX-EC-17025-IMNC-2006. The procedure for extracting analytes was carried out using the method, QuEChERS. The highest concentration of the pesticides detected in the samples obtained from the non-Certified Supply Center were Chlorpyrifos 0.309 mg/kg (MRL 0.01), Dimetomorf I 0.029 mg/kg (MRL 0.01), Malathion 0.155 mg/kg (MRL 0.01), Omethoate 0.032 (MRL 0.01), Carbendazim 0.090 mg/kg (MRL 0.01), and Imidacloprid 0.058 mg/kg (MRL 0.01). Thirty percent of the samples analyzed showed pesticide residues; the most frequent were Carbendazim. The results for the estimated daily intake (EDI) oscillated between 6.5 × 10−5 and 1.3 × 10−4 mg/kg body weight for the vegetable, Nopal. In principle, it could be concluded that the consumption of Nopal with pesticide residues does not represent any toxicological risk for human health, however, the risk cannot be ruled out due to the intake of other vegetables and fruits that are cultivated in the Mexican Republic, which probably present pesticide residues, which together would raise potential risks to human health. [ABSTRACT FROM AUTHOR]