Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

Abstract

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23]. This type of sensor can measure the spectral response of a target at very-high resolutions while having a reductive amount of radiometric interference by being near the leaf sample.…”

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

“…Thus, it is difficult to adopt the traditional analysis as a recurrent procedure to monitor multiple areas and stages [19]. As a rapid, nondestructive, and highly-replicable method, UAV-based image analysis may be of assistance to perform plant nutrient content and growth-status estimate [20][21][22][23][24].…”

“…Predicting nutrient content and plant height with remote systems and automated intelligent methods is gaining attention in agriculture practices. With multispectral sensors, at canopy or leaf levels, different studies predicted leaf nitrogen concentration (LNC) in maize (Zea mays L.) [16], winter-wheat (Triticum aestivum) [21], cotton (Gossypium hirsutum) [22], rice (Oryza sativa) [23], citrus (Citrus sinensis) [18,24], among others. Although hyperspectral sensors stand out in their ability to characterize the spectral response with high accuracies [25,26], multispectral sensors are used more frequently in agriculture remote sensing since they are economically viable and accessible to most of the front-end users.…”

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

“…The usage of remote sensing systems supports data acquisition in a more frequent and faster manner, being more valuable to evaluate plants than most traditional agronomic procedures [1,2]. In the nutritional analysis, different remote sensing techniques were evaluated recently [3][4][5][6][7]. Regardless of the conducted approach, the spectral analysis of the vegetation is viewed as a reasonable alternative to estimate plant health conditions.…”