Advanced Sensor Technology for Enhanced Plant Stress Monitoring

By Ankit SinghReviewed by Susha Cheriyedath, M.Sc.May 15 2024

In the evolving world of agriculture, ensuring the health and vitality of crops is crucial. With constantly changing environmental conditions such as climate shifts and variations in soil quality, plants are exposed to numerous stressors that impact their growth and productivity. To tackle this challenge, researchers and farmers have turned to innovative technologies like sensors for monitoring plant stress. These sensors provide real-time insights into the physiological state of plants, allowing for timely interventions to reduce stress and improve crop yields.

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The use of sensors for detecting plant stress has evolved significantly over the years. In the past, basic sensors measured parameters such as soil moisture and temperature. However, with technological advancements, a more comprehensive range of sensors capable of capturing more detailed data has emerged. Today, sophisticated sensors can analyze plant properties at various levels, including spectral reflectance, gas emissions, and bioelectrical signals.¹

This article explores the latest advancements in sensor technology for monitoring plant stress, discussing various sensor types and their functionalities. It also delves into the applications and future prospects of this technology and its potential impact on agriculture.

More from AZoSensors: An Introduction to Printable Sensors

Unveiling Plant Stress Through Spectral Analysis

One of the most promising approaches in sensor technology for detecting plant stress involves spectral analysis. Plants interact with light in unique ways depending on their health status. For instance, stressed plants exhibit changes in their reflectance properties across the light spectrum, from visible (red, green, blue) to near-infrared (NIR) and even ultraviolet (UV) wavelengths. These spectral analysis techniques are often non-invasive and can be deployed on various platforms, including ground-based sensors, drones, and satellites, making them highly versatile tools in agriculture.¹

Multispectral and Hyperspectral Imaging-Based Sensors

These imaging techniques capture data at multiple specific wavelengths (multispectral) or across a broad range of wavelengths (hyperspectral), allowing the detection of subtle changes in plant reflectance that may be invisible to the naked eye. A recent study demonstrated the effectiveness of multispectral imaging in identifying water stress and heat stress in maize crops.²

Hyperspectral imaging offers even more detailed information, enabling the detection of various stress factors, including nutrient deficiencies and pest infestations.^1,3

Fluorescence-Based Sensors

Plants naturally emit fluorescence, a form of light emitted at a longer wavelength after absorbing light at a shorter wavelength. Changes in fluorescence emission can indicate plant stress.

For example, chlorophyll fluorescence sensors measure the light emitted by chlorophyll molecules during photosynthesis. Researchers can assess photosynthetic efficiency and detect stress-related anomalies by analyzing fluorescence patterns.

Recent studies have shown that chlorophyll fluorescence sensors effectively detect early signs of drought stress in crops such as maize and soybeans. Additionally, a recent study demonstrated the successful use of fluorescence imaging for detecting chilling injury in tomato seedlings.^3,4

Temperature Sensors for Stress Monitoring

Monitoring leaf temperature provides valuable insights into plants' physiological state, particularly their water status and transpiration rates. By tracking changes in leaf temperature, researchers can detect early signs of water stress, nutrient deficiencies, or pathogen infections, allowing for prompt interventions to prevent potential damage.

Leaf temperature sensors typically use infrared thermography to measure the surface temperature of leaves. This non-destructive technique relies on the principle that objects emit infrared radiation proportional to their temperature. Sensors can accurately determine leaf temperature variations under different environmental conditions by capturing and analyzing infrared emissions from plant leaves.⁵

Gaseous Emissions: A Window into Plant Health

Plants release different types of volatile organic compounds (VOCs) under stress. These VOCs act as signals, warning nearby plants of potential dangers. Thus, examining a plant's VOC profile can provide valuable insights into its health.

Electronic Noses and Paper-Based Sensors

Electronic noses mimic the human sense of smell using an array of sensors to detect and distinguish between different VOCs. These devices can be used to identify plant stress at an early stage, even before visible symptoms appear.⁶

On the other hand, paper-based sensors provide a low-cost and disposable option for detecting VOCs. Recent research has shown promising results in using paper-based sensors to detect plant water and salinity stress.⁶

Beyond the Surface: Physiological Sensors for Plant Stress

While most sensor technologies focus on analyzing external plant properties, recent advancements have explored the potential of sensors that directly measure plant physiological responses, offering a deeper understanding of plant stress.

Bioelectrical Impedance Spectroscopy (BIS)

This technique measures the electrical impedance of plant tissues, providing important information about the health status and stress levels of plants.⁷

Electrochemical Sensors

These sensors measure the concentration of specific ions or molecules within plant tissues. Researchers have developed sensors to detect stress-induced changes in hormone levels or reactive oxygen species (ROS) production.

In a recent Nature study, researchers developed a multimodal nanosensor for simultaneous monitoring of stress-induced salicylic acid, a plant hormone, and hydrogen peroxide (H₂O₂) in Pak choi plants.⁸

Soil Moisture Sensors

These sensors measure the water content in the soil, providing valuable information about plant water availability and drought stress. Advanced sensor technologies, such as capacitance and time-domain reflectometry (TDR), offer precise measurements within the soil profile at multiple depths.

A recent study in Sensors investigated the impact of soil moisture variability on crop water stress and yield, highlighting the importance of spatially resolved sensor data for precision agriculture applications.⁹

Integration of Sensors with AI and ML

The true power of sensor technology lies in its ability to be integrated with machine learning (ML) and artificial intelligence (AI) systems. The vast amount of data collected by sensors must be processed, analyzed, and translated into actionable insights for farmers.

AI and ML play crucial roles in extracting meaningful information from sensor data. These algorithms can be trained on large datasets of sensor readings and corresponding plant stress conditions. Once trained, the ML models can analyze real-time sensor data and identify patterns indicative of stress, allowing for early detection and intervention and minimizing yield losses.¹⁰

Applications in Agriculture

Precision Irrigation

Plant stress sensors are vital in precision irrigation systems, where water resources are optimized based on real-time plant needs. Farmers can implement targeted irrigation strategies to minimize water waste and maximize crop productivity by continuously monitoring soil moisture levels and plant physiological parameters.¹¹ 

Moreover, sensor data can trigger automated irrigation systems, adjust fertilizer application rates, or activate targeted pest control measures. For example, a sensor detecting water stress in a field can automatically trigger the irrigation system, ensuring optimal plant water availability.

Disease Management

Early detection of disease outbreaks is essential for effective disease management in agriculture. Plant stress sensors enable rapid identification of stress-related symptoms associated with pathogen infections, allowing farmers to implement timely disease control measures.¹¹

Climate Resilience

Climate change poses significant challenges to agricultural sustainability, with extreme weather events becoming more frequent and unpredictable. Integrating sensors with decision support systems that combine sensor data with weather forecasts, historical data, and other relevant information can provide farmers with personalized recommendations for managing their crops. These recommendations can help farmers adapt to changing climatic conditions by offering early warnings of heat, drought, or salinity stress.¹²

Challenges and Considerations

Despite its immense potential in agriculture, sensor technology faces some challenges hindering widespread adoption and optimal performance. One obstacle is the cost and scalability of deploying sensor networks across large fields. External elements like wind and sunlight can also affect sensor readings, necessitating calibration techniques and algorithms to account for these variations.¹³

Another challenge is the specificity of sensors; while they can detect stress generally, distinguishing between specific stress types requires targeted sensors or advanced analysis methods. Furthermore, sensors need reliable power sources, especially real-time monitoring, which demands energy-efficient solutions and user-friendly maintenance procedures.¹³

Overcoming these hurdles is essential for unlocking the potential of sensor technology, transforming plant stress detection, and promoting a sustainable future for agriculture.

Future Prospects and Conclusion

The landscape of plant stress detection is undergoing a rapid transformation fueled by advancements in sensor technology. Miniaturization and cost reductions will lead to widespread adoption, with sensors seamlessly integrating into existing agricultural machinery. By combining data from different types of sensors - spectral, gas, and physiological - a more comprehensive understanding of plant stress can be achieved, enabling precise identification of specific stressors.

Moreover, by utilizing big data analytics and interdisciplinary collaborations, researchers can gain new insights into plant stress dynamics and develop innovative solutions to ensure sustainable food production for future generations.

In conclusion, plant stress sensors are critical tools for optimizing crop management practices and enhancing agricultural resilience. The integration of sensors with automation and decision-support systems is transforming agriculture from a reactive to a proactive approach. By leveraging the latest sensor technologies and scientific discoveries, farmers can make data-driven and informed decisions to mitigate stressors, increase crop yields, and ensure global food security in the face of environmental challenges.

References and Further Reading

1. Berger, K. et. al., (2022). Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sensing of Environment, 280, 113198. https://doi.org/10.1016/j.rse.2022.113198
2. Brewer, K. et al., (2022). Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform. Drones, 6(7), 169. https://doi.org/10.3390/drones6070169
3. Sobejano-Paz et. al., (2020). Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought. Remote Sensing, 12(19), 3182. https://doi.org/10.3390/rs12193182
4. Dong, Z., Men, Y., Li, Z., Liu, Z., & Ji, J. (2021). Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering. Transactions of the ASABE, 64(1), 13–22. https://doi.org/10.13031/trans.13212
5. Pineda, M., Barón, M., & Pérez-Bueno, M.-L. (2020). Thermal Imaging for Plant Stress Detection and Phenotyping. Remote Sensing, 13(1), 68. https://doi.org/10.3390/rs13010068
6. Murali-Baskaran, R. K., Mooventhan, P., Das, D., Dixit, A., Sharma, K. C., Senthil-Nathan, S., Kaushal, P., & Ghosh, P. K. (2022). The Future of Plant Volatile Organic Compounds (pVOCs) Research: Advances and Applications for Sustainable Agriculture. Environmental and Experimental Botany, 104912. https://doi.org/10.1016/j.envexpbot.2022.104912
7. Christian Nouaze, J. (n.d.). Bioelectrical Impedance Spectroscopy (BIS) Monitoring of Lettuce during 19 Hours. sciforum. https://doi.org/10.3390/I3S2021Dresden-10123
8. Ang, M. C.-Y., Saju, J. M., Porter, T. K., Mohaideen, S., Sarangapani, S., Khong, D. T., Wang, S., Cui, J., Loh, S. I., Singh, G. P., Chua, N.-H., Strano, M. S., & Sarojam, R. (2024). Decoding early stress signaling waves in living plants using nanosensor multiplexing. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-47082-1
9. Lloret, J., Sendra, S., Garcia, L., & Jimenez, J. M. (2021). A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture. Sensors, 21(21), 7243. https://doi.org/10.3390/s21217243
10. Zubler, A. V., & Yoon, J.-Y. (2020). Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning. Biosensors, 10(12), 193. https://doi.org/10.3390/bios10120193
11. Yang, C. (2020). Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example. Engineering, 6(5), 528–532. https://doi.org/10.1016/j.eng.2019.10.015
12. Rivero, R. M., Mittler, R., Blumwald, E., & Zandalinas, S. I. (2021). Developing climate‐resilient crops: Improving plant tolerance to stress combination. The Plant Journal. https://doi.org/10.1111/tpj.15483 
13. Lo Presti, D., Di Tocco, J., Massaroni, C., Cimini, S., De Gara, L., Singh, S., Raucci, A., Manganiello, G., Woo, S. L., Schena, E., & Cinti, S. (2023). Current understanding, challenges, and perspective on portable systems applied to plant monitoring and precision agriculture. Biosensors and Bioelectronics, 222, 115005. https://doi.org/10.1016/j.bios.2022.115005

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