In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

Water is crucial for enduring horticultural productivity, but high water-use requirements and declining water supplies with the changing climate challenge economic viability, environmental sustainability, and social justice. While the scholarly literature pertaining to water management in horticulture abounds, knowledge of practices and technologies that optimize water use is scarce. Here, we review the scientific literature relating to water requirements for horticulture crops, impacts on water resources, and opportunities for improving water- and transpiration-use efficiency. We find that water requirements of horticultural crops vary widely, depending on crop type, development stage, and agroecological region, but investigations hitherto have primarily been superficial. Expansion of the horticulture sector has depleted and polluted water resources via overextraction and agrochemical contamination, but the extent and significance of such issues are not well quantified. We contend that innovative management practices and irrigation technologies can improve tactical water management and mitigate environmental impacts. Nature-based solutions in horticulture—mulching, organic amendments, hydrogels, and the like—alleviate irrigation needs, but information relating to their effectiveness across production systems and agroecological regions is limited. Novel and recycled water sources (e.g., treated wastewater, desalination) would seem promising avenues for reducing dependence on natural water resources, but such sources have detrimental environmental and human health trade-offs if not well managed. Irrigation practices including partial root-zone drying and regulated deficit irrigation evoke remarkable improvements in water use efficiency, but require significant experience for efficient implementation. More advanced applications, including IoT and AI (e.g., sensors, big data, data analytics, digital twins), have demonstrable potential in supporting smart irrigation (focused on scheduling) and precision irrigation (improving spatial distribution). While adoption of technologies and practices that improve sustainability is increasing, their application within the horticultural industry as a whole remains in its infancy. Further research, development, and extension is called for to enable successful adaptation to climate change, sustainably intensify food security, and align with other Sustainable Development Goals.

Autonomous robots in the agri-food sector are increasing yearly, promoting the application of precision agriculture techniques. The same applies to online services and techniques implemented over the Internet, such as the Internet of Things (IoT) and cloud computing, which make big data, edge computing, and digital twins technologies possible. Developers of autonomous vehicles understand that autonomous robots for agriculture must take advantage of these techniques on the Internet to strengthen their usability. This integration can be achieved using different strategies, but existing tools can facilitate integration by providing benefits for developers and users. This study presents an architecture to integrate the different components of an autonomous robot that provides access to the cloud, taking advantage of the services provided regarding data storage, scalability, accessibility, data sharing, and data analytics. In addition, the study reveals the advantages of integrating new technologies into autonomous robots that can bring significant benefits to farmers. The architecture is based on the Robot Operating System (ROS), a collection of software applications for communication among subsystems, and FIWARE (Future Internet WARE), a framework of open-source components that accelerates the development of intelligent solutions. To validate and assess the proposed architecture, this study focuses on a specific example of an innovative weeding application with laser technology in agriculture. The robot controller is distributed into the robot hardware, which provides real-time functions, and the cloud, which provides access to online resources. Analyzing the resulting characteristics, such as transfer speed, latency, response and processing time, and response status based on requests, enabled positive assessment of the use of ROS and FIWARE for integrating autonomous robots and the Internet.

Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Specifically, six of the most common machine learning algorithms were applied (i.e., logistic regression, k-nearest neighbors, decision tree, random forest, neural network, and support-vector machine). The proposed method was able to precisely recognize crops and weeds throughout a wide cultivation field, training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture.