Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

Abstract

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

Estimation of Winter Wheat Plant Nitrogen Concentration from UAV Hyperspectral Remote Sensing Combined with Machine Learning Methods

“…The development of remote sensing makes it possible to diagnose crop nitrogen status without damage and at low cost. Researchers tried to monitor the crop nitrogen index using proximal remote sensing sensors such as Dualex 4 (Force-A, Orsay, Paris, France) [16][17][18], GreenSeeker (Trimble, Westminster, CO, USA) [3,19,20], Multiplex ® 3 (Dynamax, Elkhart, IN, USA) [21], and RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA) [22,23] and achieved encouraging research results. Although the proximal sensing technology has incomparable advantages, such as being free from the interference of background factors such as light and soil [15], it still needs to repeat a lot of measurement work in crop canopy and can only obtain single point information of field crops, which is difficult to achieve regional scale nitrogen nutrition monitoring.…”

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

“…This fluorescence is closely associated with plant photosynthetic activity and can be used to monitor crop N status, even in the early growth stages. [36][37][38] These sensors have numerous applications, including the measurement of flavonol content in plants and other biological samples. Flavonols are a group of plant secondary metabolites that play important roles in plant development and defense against biotic and abiotic stresses.…”

“…This wavelength is absorbed by chlorophyll in the focused sample. Similarly, fluorescence sensors 18,38,41,43 employ an LED emitting light at a specific wavelength (218 nm − 735 nm) matching the excitation peak of the fluorophore. 173,174 A constant current source drives the LED to ensure a stable light output.…”

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

“…With the development of remote sensing technology, the monitoring of plant nitrogen has new technical support. Ground-based remote sensing has the irreplaceable advantages of being free from sunlight, soil, and weeds [10], such as when using Multiplex ® 3 (Dynamax, Elkhart, IN, USA) [11], Dualex 4 (Force-A, Orsay, Paris, France) [12][13][14], and other proximal remote sensing sensors, and has produced excellent results. However, it still requires a lot of repetitive work at the test site and only obtains spectral information from a single location, making it difficult to realize nitrogen content monitoring on a large regional scale.…”