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This paper presents a machine vision system (MVS) for visual quality inspection of beans which is composed by a set of software and hardware. The software was built from proposed approaches for segmentation, classification, and defect detection, and the hardware consists of equipment developed with low-cost electromechanical materials. Experiments were conducted in two modes: offline and online. For offline experiments, aimed at evaluating the proposed approaches, we composed a database containing 270 images of samples of beans with different mixtures of skin colors and defects. In the online mode, the beans contained in a batch, for example, a bag of 1 kg, are spilled continuously on the conveyor belt for the MVS to perform the inspection, similar to what occurs in an automated industrial visual inspection process. In the offline experiments, our approaches for segmentation, classification, and defect detection achieved, respectively, the average success rates of 99.6%, 99.6%, and 90.0%. In addition, the results obtained in the online mode demonstrated the robustness and viability of the proposed MVS, since it is capable to analyze an image of 1280 × 720 pixels, spending only 1.5 s, with average successes rates of 98.5%, 97.8%, and 85.0%, respectively, to segment, classify, and detect defects in the grains contained in each analyzed image.

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

With the advent of Industry 4.0, the use of new technologies, robotization and advanced manufacturing has been extended to the agricultural sector, with the aim of increasing productivity, reducing environmental impacts, increasing profits and improving product quality from where emerged the terms Precision Agriculture, Agribusiness 4.0, Agriculture 4.0 and Agri-industry-4.0. However, while much is being said about adopting new technologies in the stages of soil preparation, planting and harvesting, little is said about the processing of agricultural products using, for example, automated systems for visual quality inspection. This work aims to investigate the different approaches for automatic visual inspection of grain quality proposed in the last decade and present a discussion about how these approaches are inserted in the context of these new productive processes of modern agriculture, as well as the positive aspects and limitations found for their uses.

Background: The growing global demand for grains and the pursuit of greater efficiency in agroindustrial production processes have fueled scientific interest in technologies for automatic visual inspection of agricultural grains (AVIAG). Despite the increasing number of studies on this topic, few have addressed the practical implementation of these technologies within industrial environments. Objective: This study aims to investigate the technological demands, analyze the potential applications, and identify the challenges for technology transfer of AVIAG technologies to the agroindustrial sector. Methods: The methodological approach combined a comprehensive literature review, which enabled the mapping of AVIAG technology applications and technological maturity levels, with a structured survey designed to identify practical demands, challenges, and barriers to technology transfer in the agricultural sector. Results: The results show that most of the proposed solutions exhibit low technological maturity and require significant adaptation for practical application, which undermines the discussion on technology transfer. Conclusions: The main barriers to large-scale adoption of AVIAG technologies include limited dissemination of scientific knowledge, a shortage of skilled labor, high implementation costs, and resistance to changes in production processes. Nonetheless, the literature highlights benefits, such as increased automation, enhanced operational efficiency, and reduced post-harvest losses, which reinforce the potential of AVIAG technologies in advancing the modernization of the agroindustrial sector.

The significant volume of vehicular traffic has been considered one of the main causes of air pollution due to the rapid growth of urbanization and motorization in the world. This trend has instigated efforts to search for sustainable solutions aimed not only at mitigating the deleterious consequences stemming from air pollution but also at implementing efficacious urban mobility strategies and policies. In this context, the present study endeavors to explore the modeling and predicting of hospitalizations and associated costs linked to respiratory diseases, influenced by vehicular pollutants within the urban milieu of São Paulo—a city renowned for harboring one of the largest vehicular fleets globally. Specifically, an adaptive neuro-fuzzy inference system (ANFIS) was developed based on pollutant data encompassing carbon monoxide (CO), Particulate matter with diameters less than 10 µm (PM 10 ), Particulate matter with diameters less than 2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ), oone (O 3 ), and sulfur dioxide (SO 2 ), emitted within the city confines spanning the period from 2011 to 2019. The simulations conducted revealed that with knowledge of the monthly concentrations of the analyzed pollutants, it was feasible to forecast hospitalization rates and costs with an error lower than 6%. Additionally, scenarios illustrating the applicability of ANFIS in public health management and its contributions to the United Nations Sustainable Development Goals (SDGs) are presented and discussed. Graphical abstract

With the advent of Industry 4.0, the use of new technologies, robotization and advanced manufacturing has been extended to the agricultural sector, with the aim of increasing productivity, reducing environmental impacts, increasing profits and improving the quality of products, giving rise to the terms Precision Agriculture, Agribusiness 4.0, Agriculture 4.0 and Agroindustry 4.0. If on the one hand much is being said about the adoption of new technologies in the stages of land preparation, planting and harvesting, on the other hand very little is said about the processing of agricultural products using, for example, automated systems for visual inspection of quality. This work aims to investigate the different approaches for automatic visual inspection of grains quality proposed in the last decade and present a discussion about how these approaches are inserted in the context of these new productive processes of modern agriculture, as well as the positive aspects and the limitations found for their uses.

With the advent of Industry 4.0, the use of new technologies, robotization and advanced manufacturing has been extended to the agricultural sector, with the aim of increasing productivity, reducing environmental impacts, increasing profits and improving product quality from where emerged the terms Precision Agriculture, Agribusiness 4.0, Agriculture 4.0 and Agri-industry-4.0. However, while much is being said about adopting new technologies in the stages of soil preparation, planting and harvesting, little is said about the processing of agricultural products using, for example, automated systems for visual quality inspection. This work aims to investigate the different approaches for automatic visual inspection of grain quality proposed in the last decade and present a discussion about how these approaches are inserted in the context of these new productive processes of modern agriculture, as well as the positive aspects and limitations found for their uses. Keywords: Agroindustry 4.0; Industry 4.0; Agriculture 4.0; Automatic Visual Inspection; Grains; Agricultural Sector. Com o advento da Industria 4.0, o emprego das novas tecnologias, da robotizacao e da manufatura avancada tem sido estendido para o setor agricola, com o objetivo de aumentar produtividade, diminuir os impactos ambientais, aumentar os lucros e melhorar a qualidade dos produtos, dando origem aos termos Agricultura de Precisao, Agronegocio 4.0, Agricultura 4.0 e Agroindustria-4.0. Contudo, se por um lado muito se fala sobre a adocao de novas tecnologias nas etapas de preparacao do solo, plantio e colheita, pouco se fala sobre o beneficiamento dos produtos agricolas usando, por exemplo, sistemas automatizados para inspecao visual de qualidade. Este trabalho tem como objetivo investigar as diferentes abordagens para inspecao visual automatica da qualidade de graos propostas na ultima decada e apresentar uma discussao sobre como tais abordagens se inserem no contexto desses novos processos produtivos da agricultura moderna, bem como os aspectos positivos e as limitacoes encontradas para suas utilizacoes. Palavras Chave: Agroindustria 4.0; Agronegocio 4.0; Industria 4.0; Inspecao Visual Automatica; Graos.