Technologies for Improved Nutrient Analysis

Nutrient management is important for citrus production. Regular nutrient assessments should be conducted to optimize nutrient balance and prevent deficiencies or over-fertilization. Optimizing nutrition is important for tree health and can improve tolerance to stresses and diseases.

Good nutrient management requires regular field monitoring to identify problems and examine crop responses. Leaves need to be collected and sent to a specialized laboratory to get a detailed analysis of macronutrients and micronutrients, which is time-consuming and costly.

It is recommended to conduct nutrient analyses in July and August after the spring flush (see edis.ifas.ufl.edu/pdf/SS/SS53100.pdf), but more frequent analyses of leaf nutrients may be necessary to determine deficiencies associated with huanglongbing or other biotic and abiotic factors. Additional analyses may also be necessary where responses to novel management practices need to be monitored. However, it is not economically feasible to frequently collect and analyze leaves for plant nutrient status.

In addition to being time-consuming and costly, leaf nutrient analysis is prone to human error because of inconsistencies and bias during leaf sampling and the analysis process, which can compromise the interpretation and relevance of the data. Faster and cheaper alternatives to conventional nutrient analysis methods are being developed at a rapid pace.

DRONES AND ARTIFICIAL INTELLIGENCE

New technologies like unmanned aerial vehicles (UAVs or drones) and artificial intelligence (AI) can be utilized to develop a more efficient methodology to determine leaf nutrient concentrations and improve the speed of data collection and consistency. Researchers at the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) Southwest Florida Research and Education Center (SWFREC) developed a non-destructive method that can be quickly and efficiently used to determine citrus leaf nutrients and create fertility maps that are compatible with variable-rate fertilizer applicators. This novel method can help overcome or complement some of the limitations of traditional leaf nutrient analysis methods.

Spectral reflectance (i.e., the energy a surface reflects at a specific wavelength) of citrus canopies in five bands of light (red, green, blue, red edge and near-infrared) were used to create an AI-based model to determine plant nutrient concentrations. The data were collected with a quadcopter UAV equipped with a multispectral camera (Figure 1).

A large dataset with good variability was developed by analyzing four large-acreage commercial field trials in two different citrus production areas (Central Ridge and Southeast Florida) with two different scions (Hamlin and Valencia orange) and a diversity of rootstocks. The differences in location, grove management and scion?rootstock combination affect nutrient uptake and distribution in the tree canopy, which made the dataset sufficiently robust to develop a precise predictive model.

The framework of this study was divided into two main phases. In the first phase (data acquisition), researchers acquired the spectral measurements of the canopy reflectance with a UAV-based multispectral camera. Leaf samples were collected, and nutrients were analyzed in the laboratory to generate the dataset. The second phase (model building and validation) consisted of: 1) a pre-analysis to evaluate the dataset for each nutrient and 2) model development and evaluation to ensure the repeatability of the methodology used.