2024/09/20 by Mark Cardamis, Hong Jia, Cardamis, Mark +15 · 1 citation
Agricultural and Biological Sciences · Environmental Science · #FOS: Electrical engineering #Leaf Properties and Growth Measurement #Signal Processing (eess.SP) #Smart Agriculture and AI #Water Quality Monitoring Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.03680
openalex publication_date 2024/09/20 · openalex created_date 2024/10/12 · openalex updated_date 2026/07/28
Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content. While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this paper, we introduce Leafeon, which leverages mmWave radar to measure leaf water content non-invasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf water content estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a Mean Absolute Error (MAE) of leaf water content as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments (see Fig. 1). Our results showed a strong correlation between predicted leaf water content levels and drought events.