Publications

Special report: The Internet of Things for Precision Agriculture (IoT4Ag)

The National Science Foundation (NSF) Engineering Research Center (ERC) for the Internet of Things for Precision Agriculture (IoT4Ag) was established on September 1, 2020 and launched its collaborative programs across the four NSF ERC pillars of convergent research, engineering workforce development, diversity and culture of inclusion, and innovation ecosystem. IoT4Ag unites an interdisciplinary cadre of faculty and students from the University of Pennsylvania, Purdue University, the University of California-Merced, and the University of Florida, with partners in education, government, industry, and the end-user farming community. Read more.

Cherie R. Kagan, David P. Arnold, David J. Cappelleri, Catherine M. Keske, Kevin T. Turner (2022). Special report: The Internet of Things for Precision Agriculture (IoT4Ag), Computers and Electronics in Agriculture.

Published in Computers and Electronics in Agriculture.

DOI: 10.1016/j.compag.2022.106742

Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

Mobile robots such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) are increasingly used for precision agriculture. However, it remains a challenging task to develop a reliable yet fully autonomous UAV system that can actively extract actionable information in large-scale cluttered agricultural environments. In this survey, we review recent advances in UAV hardware and software, ranging from novel platforms and sensors to state-of-the-art autonomous navigation, object detection and segmentation, robot localization, and mapping algorithms related to agriculture. Read more.

Xu Liu, Steven W Chen, Guilherme V Nardari, Chao Qu, Fernando Cladera Ojeda, Camillo J Taylor, Vijay Kumar (2022). Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture.

Published in IEEE Micro.

DOI: 10.1109/MM.2021.3134744

AgBug: Agricultural Robotic Platform for In-Row and Under Canopy Crop Monitoring and Assessment

This paper focuses on the development of a small scale agricultural robotic platform with advantages over the current agricultural phenotyping platforms that lack the size-scale and sensor resolution needed to study hard to reach under-canopy row crops. The AgBug utilizes a sensor suite consisting of a LiDAR and RGB camera for crop monitoring on a 12″ × 9″ footprint platform. The main challenge for this platform design is not only its compact size and portability, but its ability to navigate and obtain geo-referenced and time-tagged data in the GNSS-denied environment that exists under the crop canopy. Read more.

R. Manish, Z. An, A. Habib, M. Tuinstra, D. Cappelleri (2021). AgBug: Agricultural Robotic Platform for In-row and Under Canopy Crop Monitoring and Assessment.

Published in Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE).

DOI: 10.1115/DETC2021-68143

Peanut disease epidemiology under dynamic microclimate conditions and management practices in North Florida

Diverse field characteristics, weather patterns, and management practices can result in variable microclimates. The objective or this study was to relate in-field microclimate conditions with peanut diseases and yield and determine the effect of irrigation and fungicides within these environments. Results indicate that disease prediction models built on dynamic environmental factors in the context of multiple pathogens and natural field conditions could be developed to improve within-season management decisions for more efficient fungicide inputs. Read more.

Barocco, R. L., Sanjel, S., Dufault, N. S., Barrett, C., Broughton, B., Wright, D. L., and Small, I. M. (2021). Peanut disease epidemiology under dynamic microclimate conditions and management practices in North Florida.

Published in Plant Disease.

DOI: 10.1094/PDIS-11-20-2390-RE

Potential of ozonated-air (OA) application to reduce the weight and volume loss in fresh figs

This study showed that ozonated air (OA) treatment of fresh figs can be used to minimize losses and improve the overall quality of fresh figs by investigating the effect of OA on weight loss, volume reduction (shriveling), and skin firmness on fresh figs and monitoring the changes in the epidermis of OA-treated fruits in storage. In Phase 1 of the study, fresh Kadota figs at two different maturity levels, commercial-ripe and tree-ripe, were exposed to OA for up to 11 h at room temperature to find the optimum parameters for ozone treatment. In Phase 2, Kadota and Black Mission figs were treated with OA (15 ppm for 3 h) and their aging parameters were evaluated. Read more.

Fig. 1

Afsah-Hejri L., A. Toudeshki, T. Homayouni, S. Mehrazi, A. Gholami Pareh, P. Gordon, R. Ehsani. 2021. Potential of ozonated-air (OA) application to reduce the weight and volume loss in fresh figs (Ficus carica L.).

Published in Postharvest Biology and Technology.

DOI: 10.1016/j.postharvbio.2021.111631

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. Read more.

R. Sahay, C. G. Brinton, and D. J. Love (2021). A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification.

Published in IEEE Transactions on Cognitive Communications and Networking.

DOI: 10.1109/TCCN.2021.3114154

The central role of ear nitrogen uptake in maize endosperm cell and kernel weight determination during the lag period

Although kernel weight (KW) has proven to be an increasingly important driver behind grain yield (GY) variability in modern maize hybrids, nitrogen’s (N) role in the determination of individual sink capacity (i.e., potential KW) during the lag phase of reproductive development remains unclear. The research objective was to study the relationships between endosperm cell number (ECN) during the lag phase (an indicator of potential KW) and final KW within the context of changing plant N dynamics in field-grown maize during the lag phase.  This study shows that higher N availability, independently of N timing and plant density, increased final KW by enhancing the sink capacity of individual kernels (at a wide range of kernel numbers per plant) via gains in ECN during lag-period development. Read more.

Olmedo Pico, L.B., Zhang, C., Vyn, T.J. (2021). The central role of ear nitrogen uptake in maize endosperm cell and kernel weight determination during the lag period.

Published in Field Crops Research.

DOI: 10.1016/j.fcr.2021.108285

FADS: A framework for autonomous drone safety using temporal logic-based trajectory planning

In this work, we present an integrated Framework for Autonomous Drone Safety (FADS). As surface congestion increases and the technology surrounding unmanned aerial systems (UAS) matures, more people are looking to the urban airspace and Urban Air Mobility (UAM) as a piece of the puzzle to promote mobility in cities. However, the lack of coordination between UAS stakeholders, federal UAS safety regulations, and researchers developing UAS algorithms continues to be a critical barrier to widespread UAS adoption. FADS takes into account federal UAS safety requirements, UAM challenge scenarios, contingency events, as well as stakeholder-specific operational requirements. Read more.

Yash Vardhan Pant, Max Z. Li, Rhudii A. Quaye, Alena Rodionova, Houssam Abbas, Megan S. Ryerson, Rahul Mangharam (2021). FADS: A framework for autonomous drone safety using temporal logic-based trajectory planning.

Published in Transportation Research Part C: Emerging Technologies.

DOI: 10.1016/j.trc.2021.103275

Entrepreneurial Talent Building for 21st Century Agricultural Innovation

Figure 1Agricultural innovation is a key component of the global economy and enhances food security, health, and nutrition. Current innovation efforts focus mainly on supporting the transition to sustainable food systems, which is expected to harness technological advances across a range of fields. In this Nano Focus, we discuss how such efforts would benefit from not only supporting farmer participation in deciding transition pathways but also in fostering the interdisciplinary training and development of entrepreneurial-minded farmers, whom we term “AgTech Pioneers”, to participate in cross-sector agricultural innovation ecosystems as cocreators and informed users of developing and future technologies. Read more.

Yoon BK, Tae H, Jackman JA, Guha S, Kagan CR, Margenot AJ, Rowland DL, Weiss PS, Cho NJ (2021). Entrepreneurial Talent Building for 21st Century Agricultural Innovation.

Published in ACS Nano.

DOI: 10.1021/acsnano.1c05980