Abstract
Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient and stress dynamics. This research aims to develop and validate a multiplexed sensing platform for real-time, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, functionalized with non-enzymatic electrode coatings, were deployed in bell pepper plants grown under four treatment combinations of irrigation (full vs. deficit) and nitrogen application (low vs. high). Data was collected every three hours over the growing period and analyzed using a long short-term memory (LSTM) model for short-term prediction of nutrient and hormone fluctuations. The sensors exhibited high sensitivity and stability, achieving detection limits of 0.22 μM for IAA, 1.07 μM for MeJA, 1.31 μM for SA, 1.08 ppm for nitrate, 1.02 ppm for ammonium, 0.29 ppm for ET, and 0.01 pH unit. The LSTM model demonstrated strong predictive capability (R² = up to 0.86), accurately forecasting shortterm variations in plant and soil nutrient-hormone profiles. These findings demonstrate that coupling real-time, multiplexed sensing with machine learning enables prediction of crop stress, supporting precision nitrogen management and advancing sustainable agricultural practices.
Description
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Publisher
Elsevier
Date of publication
2026
Language
english
Persistent identifier
http://hdl.handle.net/10950/5169
Document Type
Article
Recommended Citation
Hossain, Nafize I.; Solaiman, Mohammad; Habib, A.K.M. Ahsanul; Hossain Al Hadi, Md Al Mahmud; and Tabassum, Shawana, "Stem-FIT and Soil-FIT: Integrated plant and soil nitrogen-hormone sensing with machine learning-based forecasting for next-generation precision agriculture" (2026). Electrical Engineering Faculty Publications and Presentations. Paper 32.
http://hdl.handle.net/10950/5169