Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Abstract

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

A leaf reflectance-based crop yield modeling in Northwest Ethiopia

“…In addition, EVI and NDVI performed well in predicting crop grain and biomass yield with spectral response [ 70 , 78 – 82 ]. However, the low performance of RNDVI and SAVI in estimating maize yields could be related to the variation in various factors, including biotic and abiotic factors [ 83 ] and the saturation of the vegetation indices [ 84 – 86 ].…”

“…Maize grain yield was also best predicted with GNDVI and NDVI [ 21 , 22 , 71 , 91 ]. Furthermore, SAVI and NDVI predicted maize yields better [ 86 , 87 , 92 ].…”

“…The highest scoring parameters were discovered. The involvement of the different SVIs to success, on the other hand, could differ depending on their interactions with the growth characters of the maize plants, including chlorophyll concentration and moisture content [ 71 , 86 – 87 , 92 ].…”

Development of a Technique for Classifying Photovoltaic Panels Using Sentinel-1 and Machine Learning

“…The datasets in this study to which the YOLOv3 and YOLOv5 algorithms were applied were developed from a total of 13,152 images with a size of 128 × 128. Learning and validation datasets were constructed at a ratio of approximately 7 : 3, following a previous study [58]. For YOLOv3, Darknet-53 was used as the backbone network, while for YOLOv5, training was carried out based on the YOLOv5l model.…”

A comparison of traditional and machine learning corn yield models using hyperspectral UAS and Landsat imagery

“…Several studies have focused on the exploitation of remote sensing data and indices for use in constructing mathematical models for forecasting maize silage yield. [5][6][7][8][9][10][11][12] Two examples of vegetation indices (VIs) that have shown promise as predictors are the normalized difference vegetative index (NDVI) and enhanced vegetative index (EVI). Both indices are regularly used in linear and exponential regression models:…”