Determining nitrogen deficiencies for maize using various remote sensing indices

Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions

Crop cultivation provides ecosystem services on increasingly large fields. However, the effects of in-field spatial heterogeneity on crop yields, in particular triticale, have rarely been considered. The study assess the effects of in-field soil heterogeneity and elevation on triticale grown in an intensively cropped hummocky landscape. The field was classified into three soil classes: C1, C2, and C3, based on soil texture and available water capacity (AWC), which had high, moderate, and low yield potential, respectively. Three elevations (downslope (DS), midslope (MS), and upslope (US)) were considered as the second study factor. An unbalanced experimental design was adopted with a factorial analysis of variance for data analysis. Temporal growth analysis showed that soil classes and elevation had significant effects. Generally, better growth was observed in C1 compared to that of C3. DS had a lower yield potential than that of MS and US. In addition, the interactive effect was confirmed, as triticale had poor growth and yield in C3 on the DS, but not on US. Crop physiological parameters also confirmed the differences between soil classes and elevation. Similarly, soil moisture (SM) content in the plow layer measured at different points in time and AWC over the soil profile had a positive association with growth and yield. The results confirmed that spatial differences in AWC and SM can explain spatial variability in growth and yield. The mapping approach combining soil auguring techniques with a digital elevation model could be used to subdivide fields in hummocky landscapes for determining sub-field input intensities to guide precision farming.

Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either ‘Hamlin’ or ‘Valencia’ sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the ‘Hamlin’ and ‘Valencia’ trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for ‘Hamlin’ and ‘Valencia’ trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species.