This approach delivers improved accuracy for key indicators, enabling more reliable drought assessment and potentially supporting smarter irrigation management.
Water stress is a persistent challenge for rice production, particularly under increasingly erratic climate conditions. Accurately assessing drought impacts in rice paddies is difficult due to complex microclimates and fluctuating irrigation patterns. Rice fields managed with alternating wet and dry irrigation conserve water but create spatially and temporally variable soil moisture conditions. Traditional water stress evaluation often relies on single indicators, such as leaf water content or canopy temperature, which can overlook subtle physiological and structural differences across plant parts. While ground-based spectral measurements and vegetation indices have been widely used to monitor crop status, they can struggle with complexity and noise. Physical models such as PROSAIL simulate canopy light interactions, but alone may not capture the full phenotypic response to water stress. Conversely, data-driven approaches like machine learning can adapt to specific datasets but require large volumes of measured data. Hybrid approaches, combining physical modeling and machine learning, offer a promising path forward for accurately identifying and quantifying drought impacts.
A study (DOI: 10.1016/j.plaphe.2025.100016) published in Plant Phenomics on 28 February 2025 by Yuanyuan Zha ’s team, Wuhan University, demonstrates that integrating radiative transfer modeling with multidimensional imaging significantly improves the accuracy and sensitivity of rice water stress detection under complex field conditions.
Researchers conducted two years of experiments in rice fields with long-term and short-term water-deficit treatments, simulating real-world irrigation practices. Four comprehensive canopy-related indicators were examined, with particular focus on canopy chlorophyll content (CCC) and canopy equivalent water (CEW). These were retrieved through a hybrid method that combined PROSAIL radiative transfer model outputs with traits extracted from hyperspectral and front-view images. The process began with generating a synthetic dataset from PROSAIL to pre-train a machine learning model, followed by fine-tuning using measured data. By integrating spectral reflectance from hyperspectral imaging with phenotypic features such as leaf angle distribution from front-view images, the model achieved higher retrieval accuracy for CCC (R = 0.7920) and CEW (R = 0.8250) than conventional data-driven or physical inversion methods. Long-term drought primarily affected canopy structure and pigmentation, reducing leaf area index (LAI) during vegetative stages but sometimes increasing pigment concentration during reproductive stages. Short-term drought-rehydration cycles did not significantly alter leaf size or water content but slowed pigment decline, with CCC and CEW proving sensitive markers of these functional changes. The red-edge spectral region (742–841 nm) was identified as optimal for assessing drought impacts. Image-derived leaf angle parameters improved PROSAIL simulation accuracy by over 40%, while combining top- and front-view imaging yielded complementary insights into canopy traits.
The new method enables more robust detection of both immediate and delayed drought effects, offering practical value for precision irrigation scheduling and water-saving management in rice cultivation. CCC and CEW, in particular, integrate multiple dimensions of plant physiology—leaf area, pigmentation, and organ water content—providing a holistic view of crop status. With portable imaging devices and automated analysis pipelines, this approach could be adapted for on-farm use, allowing farmers and agronomists to monitor water stress more efficiently and intervene before yield losses occur.
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References
DOI
10.1016/j.plaphe.2025.100016
Original Source URL
https://doi.org/10.1016/j.plaphe.2025.100016
Funding information
The research was funded by the National Natural Science Foundation of China, China (52279042), the National Key Research and Development program of China, China (2021YFC3201204), and the Key Research and Development Program in Guangxi, China (AB23026021).
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.