As the world’s population grows and the demand for food rises, more attention has been paid to increase crop yields and enhance global food security. Modern remote sensing technologies enable us to capture spectral features (such as NDVI) of crop canopy, which are widely used to assess crop growth, health, and stress conditions. We noticed in the literature that crop NDVI shows short-term variation within a day. Therefore, in this study, we leveraged the Spidercam Field Phenotyping Facility at University of Nebraska-Lincoln to measure and quantify the diurnal variation of canopy NDVI for corn and soybean crops. The experiments and data collection were conducted in 2022 and 2023, with canopy reflectance measured by a spectrometer (400-1000 nm) at multiple days covering different growth stages. In each day, measurements were taken at multiple time points within a time window ±3 hours centered around solar noon. Our analysis showed a clear concave-shaped, diurnal trend in NDVI for both crops, with the lowest NDVI at solar noon. More analyses will be performed to quantify this diurnal pattern and dissect the sources of variation due to solar angle and change in canopy morphology. This research will further improve the accuracy and relevance of NDVI in plant phenotyping and many other scientific disciplines and applications.

Stomatal conductance (SC) was utilized to indicate the rate of gas and water exchange through stomata on the leaf surface. When crops are experiencing water stress during the daytime, their stomata will close to prevent water loss. However, the crop will also receive less CO2 for photosynthesis under this condition. Consequently, accurate and efficient SC prediction can improve irrigation efficiency, particularly in the present day when water is scarce. The common way to estimate SC nowadays is to make predictions using other variables that can be measured quickly, such as conventional regression analysis. The limitation of conventional regression analysis is that it does not account for changes in SC when crops are subjected to both prolonged drought and high temperature stress. We intend to use time series prediction techniques, such as Long Short-Term Memory (LSTM), to determine the correlation between variables at different time scales. The objective of this study is to (1) Investigating the relationship between SC and persistent weather patterns. (2) Predict SC based on continuously collected soil moisture, plant canopy temperature and weather information. In this study, we measured the SC of soybean and maize by using a handheld leaf porometer. This was then compared to short-term historical crop SC predictions based on canopy temperature, soil moisture, and weather conditions. Autoregressive Integrated Moving Average (ARIMA) and LSTM based on Recurrent neural network (RNN) are used to predict SC, and the results are compared with the conventional modeling method. More details will be presented at the NAPPN conference.