This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production.

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 presence of micro and nanoplastics in the food chain constitutes an emergent multifactorial food safety and physiological stress problem, which must be approached with a strategic perspective since it affects public health when consuming products that have this pollutant, such as fish and crustaceans, fruits, and vegetables. In this review, the authors present the results by scientists from different disciplines who are dedicated to discovering their chemical constitution and origin, the contents of these microparticles in edible plants, the contamination of water-irrigated soils, the mechanisms that concentrate microplastics in these soils, methods to determine them, contamination of freshwater sources of cities, and the negative effect of nano and microplastics on various food products and their detrimental impact on the environment. Recent findings of plant uptake mechanisms complement this, but more research is needed.

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices.

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.