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Wireless sensor networks (WSNs) are increasingly becoming a critical tool for growers and researchers. We describe how the technology has advanced, starting with a commercially available WSN node and pushing the technology to make the data more meaningful, actionable and to add advanced irrigation control functionality. User features such as spatial views, custom charts, real-time data access, remote access, irrigation control, alerts, and plant models help create an advanced WSN system that is user centric. Growers and researchers were involved in the design process by directly communicating with the design engineers, and continuously using and testing new features, resulting in a user-centric design and experience. The results of this research are being rolled into a new line of commercial products and is continuously evolving based on user feedback and interaction.

Wireless Sensor Networks (WSNs) are an invaluable tool in agriculture. Currently these systems are good at reporting and logging data. Some recent work has focused on making the data useful and actionable. The next step in WSNs is the ability to automatically use that data for irrigation control. This can help minimize labor and reduce water usage.

Sixty years after debuting in industrial environments, robots are making their way into our everyday life. Farmers have benefited for some time from self-guided machinery including combines and harvesters. More recently, multi-purpose autonomous vehicles have started to be deployed in orchards, groves, nurseries, and other agricultural environments to automate or augment operations such as pruning, thinning, harvesting, mowing, and spraying. Successful commercialization of such vehicles will depend heavily on them being able to operate safely and avoid accidents involving humans, animals, trees, and farm infrastructure. We propose a safety architecture to guide the design and deployment of autonomous agricultural vehicles and their introduction into production environments. The architecture spans the three elements that, combined, should ensure safe operation over a wide spectrum of applications: (1) a distributed, sensor-based, intelligent decision-making system that coordinates and guides fleets of vehicles in and around orchards and other agricultural environments; (2) multimodal interfaces for workers to interact with the vehicles using natural language, gestures, and portable devices; (3) a comprehensive regulatory framework of standards for vehicle safety that covers everything from basic robotic technology to advanced behaviors. In this paper we present the fundamental aspects of this agricultural robotic safety architecture. We illustrate its application with examples of autonomous agricultural vehicles we developed in the past that lay down the path toward full introduction of safe, intelligent machines in agricultural production environments.

Hydroponics is currently a very successful technique for growing crops like lettuce and results in cleaner, healthier and cheaper yield. Requirement of periodic labor and a systematic arrangement of plants make it a good candidate for automation. Large-scale systems that automate the growing of lettuce already exist, but these require large capital investments and large footprints that limit the use to only large-scale growers. Our goal is to develop inexpensive robotic systems that a small-medium scale grower can afford to implement and that are compatible with the existing infrastructure. The robot is designed to pick and place plants and seedlings from one spot to other on an Nutrient Film Technique (NFT) system autonomously. We describe the design and construction of the gantry-based robot with an arm that moves on rails. By using this approach a single robot can serve a large number of plants in a greenhouse. We also describe algorithms for perception, robot path planning, and manipulation of densely planted leafy crops using a camera and Microsoft Kinect 3D imaging system. Results are demonstrated experimentally using a small AmHydro NFT system we have constructed in our lab.

We propose a new multi-hop routing protocol for wireless sensor networks, suited for monitoring and control applications. The aim of this research is to adapt flat and hierarchical architectures to create a new hybrid that draws on current protocol theories. The protocol uses a hybrid network structure to achieve scalability and is source initiated along with time driven reporting to reduce the number of packet transmissions. The protocol incorporates a link quality estimation algorithm, which enables only the nodes with high quality symmetric links to be chosen for routing. Route selection is calculated using both hop count and link quality as routing metrics. The protocol is also designed such that it is computationally simple, reliable, energy aware, does not impose any special hardware prerequisites and most importantly credible. Its credibility was verified by performing a series of field tests in a real world operating environment.

Wireless Sensor Networks (WSNs) are emerging as an invaluable tool in agriculture. Current WSNs are good at reporting sensor data and many provide simple tools for viewing the data. The current approach assumes that growers are familiar with how the data should look and they are trained to use the data. This work extends the usage of WSNs to provide growers with actionable results based on the data. We are currently implementing plant physiological models for specialty crops into the CMU SensorWeb system. These models predict water usage, allowing growers to precisely control irrigation schedules. The sensor nodes used in this work also have onboard control functionality for automating crop irrigation based on the model’s output. In this paper we present a framework for integrating models into WSNs and initial field results from this model driven approach.

Scarab is a prospecting rover for lunar missions to survey resources, particularly water ice, in polar craters. It is designed for the deployment of a deep coring drill and for transport of soil analysis instruments. Its chassis can transform to stabilize the drill in contact with the ground and can also adjust to ascend and descent steep slopes of unconsolidated soil. Additional features include a compact body for better thermal regulation, laser scanners for dark navigation, and power system designed for a persistent, low-capacity source. Scarab was prototyped at the Robotics Institute, has undergone mobility testing in soils laboratories and field sites leading up to an integrated system test including the RESOLVE drill and instrument suite at the PISCES lunar analogue site on Mauna Kea in Hawaii.