Data collection for forestry, timber, and agriculture relies on manual techniques which are labor-intensive and time-consuming. We seek to demonstrate that robotics offers improvements over these techniques and can accelerate agricultural research, beginning with semantic segmentation and diameter estimation of trees in forests and orchards. We present TreeScope v1.0, the first robotics dataset for precision agriculture and forestry addressing the counting and mapping of trees in forestry and orchards. TreeScope provides LiDAR data from agricultural environments collected with robotics platforms, such as UAV and mobile robot platforms carried by vehicles and human operators. In the first release of this dataset, we provide ground-truth data with over 1,800 manually annotated semantic labels for tree stems and field-measured tree diameters. We share benchmark scripts for these tasks that researchers may use to evaluate the accuracy of their algorithms. Finally, we run our open-source diameter estimation and off-the-shelf semantic segmentation algorithms and share our baseline results.The dataset can be found at https://treescope.org, and the data pre-processing and benchmark code is available at https://github.com/KumarRobotics/treescope.

Arid and semi-arid regions are characterized by the limited available water. We consider a scenario in which there is one shared water resource among multiple farms as well as a water-sharing policy. It is required to irrigate all or some of these farms currently, but there is not enough water to properly irrigate them. This paper proposes a Multi-farm Irrigation Scheduling System, called MISS. The objective is to save the farms that need urgent irrigation while still satisfying all farm owners according to the water-sharing policy. MISS uses a two-level decision-making method where it considers a priority for the farms that need immediate irrigation as well as a priority for blocks of every farm. MISS is designed based on a realistic scenario where, because of the limited shared water, we sometimes are not able to save all the farms.

The Artificial Intelligence of Things (AIoT), a combination of the Internet of Things (IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture (SA). AIoT has been adopted in many applications including agriculture, such as crop yield estimation, soil and water conservation, pest and disease detection and supply chain management. While there are plenty of studies on AIoT applications in healthcare, smart cities, manufacturing, and transportation, SA still has a small share of the reported research. This paper presents a comprehensive review of the existing literature in AIoT and Federated Learning (FL) for SA. It identifies current and potential challenges and provides research direction for the future investment in both academia and industry.

High-fidelity sensor data quality is the fundamental base of smart agriculture. Since crop information inference and cultivating strategy optimization mainly depend on data-driven methods; the data quality assessment is essential to ensure the reliability of the IoT systems for smart agriculture. The traditional data quality assessment methods focus on sensor data consistency with the costly reference truth, which is often not scalable for agricultural applications.

What was once a seasonal disturbance is becoming a more significant problem with orchard dust moving into towns and across main traffic thoroughfares. It's the act of sweeping the fruit into windrows that creates the problem by simultaneously agitating and blowing the soil along with the fruit. The soil billows up in clouds of dust, creating zero visibility. This ultimately creates a windrow that is equally filled with fruit and soil and is further exacerbated by picking up the combination of fruit and soil (within the windrow) and billowing a second round of dense dust into the surrounding air. Solutions to the dust problem focus on removing the sweepers and potentially the `pick-ups'. In this study, we are designing a new mechanism for almond sweeping and pick up with minimum dust generated by adapted sweepers in order to have minimum contact with ground using distance sensors and actively measure particulate matters (PM2.5 and PM10) using dust sensors and adjust system to reach acceptable level of generated dust according to National Ambient Air Quality Standards in almond harvesting process.

Unmanned aerial vehicles (UAVs) have the potential to significantly impact early detection and monitoring of plant diseases. In this paper, we present preliminary work in developing a UAV-mounted sensor suite for detection of citrus greening disease, a major threat to Florida citrus production. We propose a depth-invariant sensing methodology for measuring reflectance of polarized amber light, a metric which has been found to measure starch accumulation in greening-infected leaves. We describe the implications of adding depth information to this method, including the use of machine learning models to discriminate between healthy and infected leaves with validation accuracies up to 93%. Additionally, we discuss stipulations and challenges of use of the system with UAV platforms. This sensing system has the potential to allow for rapid scanning of groves to determine the spread of the disease, especially in areas where infection is still in early stages, including citrus farms in California. Although presented in the context of citrus greening disease, the methods can be applied to a variety of plant pathology studies, enabling timely monitoring of plant health-impacting scientists, growers, and policymakers.