I'm a strong advocate for plants as the best soil moisture and potential sensors. Data-driven tools like this one integrating canopy feedback provide more accurate and reliable information for crop management compared to traditional soil-based techniques.
Raw data alone isn't enough. Predictive modeling that interprets this data, along with feature engineering and data cleaning techniques, is essential.
Biophysical and statistical modeling approaches both have their merits, but a combined strategy offers a more complete picture.
Plant monitoring through proximal canopy sensing is surprisingly less widespread than soil-based methods. Cost isn't the barrier - proximal sensing can be just as affordable as soil sensors.
Satellite and drone imagery, while valuable, lack the necessary temporal resolution. We need 24/7 monitoring, similar to soil sensors, that proximal sensing offers.
Perhaps the biggest obstacle to wider adoption of plant-based methods is the lack of cohesive literature and standardized protocols for field applications. Different crops may require specific approaches, and research findings are scattered across various crops with limited focus on bringing these developments to practical use.
This post, "Can Plant Measurements Accurately Determine Available Soil Moisture in the Root Zone?", provides a deeper insight into how to use plant measurements for soil moisture monitoring and crop management.
Check out our open-source repository on the EnviTronics Lab GitHub for examples of our environmental modeling work and the Python/C++ code used:
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