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This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables such as nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small- and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Other considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed, as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, regulatory, and social factors.

The accelerated growth of computer vision techniques (CVT) has allowed their application in various disciplines, including horticulture, facilitating the work of producers, reducing costs, and improving quality of life. These techniques have made it possible to contribute to the automation of agro-industrial processes, avoiding excessive visual fatigue when undertaking repetitive tasks, such as monitoring and selecting seedlings grown in trays. In this study, an object detection model and a mobile application were developed that allowed seedlings to be counted from images and the calculation of the number of seedlings per tray. This system was developed under a CRISP-DM methodology to improve the capture of information, data processing, and the training of object detection models using data from six crops and four types of trays. Subsequently, an experimental test was carried out to verify the integration of both parts as a unified system, reaching an efficiency between 57% and 96% in the counting process.

Selenium (Se) is an essential micronutrient for humans, but most foods are Se deficient, mainly because of its low content in the soil. A Se-deficient diet results in increased susceptibility to cardiovascular disease, cancer, and hyperthyroidism. Agronomic biofortification is a good alternative to increase Se in food. This study investigated the effect of Se on the growth, yield, and biofortification of the rocket. Plants were grown in a hydroponic system. Seven Se concentrations (0, 10, 20, 30, 40, 50, and 60 µM) were evaluated using sodium selenate. Growth, yield, lipid peroxidation, hydrogen peroxide content, and the enzymatic activity of catalase and ascorbate peroxidase were influenced by the Se concentration. Considering the evaluated parameters, 10–30 µM Se promoted the best results, and with 20 µM, the higher yield. Rocket plants treated with Se in the nutrient solution were biofortified, showing Se contents of 598.96 to 1437.56 mg kg−1 in the dry mass, higher than plants cultivated in a nutrient solution without Se, which presented 167.84 mg kg−1 of Se. Se concentrations of 10–30 µM in the nutrient solution were beneficial for rocket plants, while concentrations above 50 µM were toxic to the plants.

The accelerated growth of computer vision techniques (CVT) has allowed their application in various disciplines, including horticulture, facilitating the work of producers, reducing costs, and improving quality of life. These techniques have made it possible to contribute to the automation of agro-industrial processes, avoiding excessive visual fatigue when undertaking repetitive tasks, such as monitoring and selecting seedlings grown in trays. In this study, an object detection model and a mobile application were developed that allowed seedlings to be counted from images and the calculation of the number of seedlings per tray. This system was developed under a CRISP-DM methodology to improve the capture of information, data processing, and the training of object detection models using data from six crops and four types of trays. Subsequently, an experimental test was carried out to verify the integration of both parts as a unified system, reaching an efficiency between 57% and 96% in the counting process. [ABSTRACT FROM AUTHOR]